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Li Z, Kamnitsas K, Dou Q, Qin C, Glocker B. Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation. IEEE Trans Med Imaging 2023; 42:3323-3335. [PMID: 37276115 DOI: 10.1109/tmi.2023.3282728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available at https://github.com/ZerojumpLine/JCSAugment.
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Li Z, Kamnitsas K, Ouyang C, Chen C, Glocker B. Context Label Learning: Improving Background Class Representations in Semantic Segmentation. IEEE Trans Med Imaging 2023; 42:1885-1896. [PMID: 37022408 DOI: 10.1109/tmi.2023.3242838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over background logit activations may shift across the decision boundary, leading to systematic over-segmentation across different datasets and tasks. In this study, we propose context label learning (CoLab) to improve the context representations by decomposing the background class into several subclasses. Specifically, we train an auxiliary network as a task generator, along with the primary segmentation model, to automatically generate context labels that positively affect the ROI segmentation accuracy. Extensive experiments are conducted on several challenging segmentation tasks and datasets. The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy. Code is available at https://github.com/ZerojumpLine/CoLab.
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Whitehouse DP, Monteiro M, Czeiter E, Vyvere TV, Valerio F, Ye Z, Amrein K, Kamnitsas K, Xu H, Yang Z, Verheyden J, Das T, Kornaropoulos EN, Steyerberg E, Maas AIR, Wang KKW, Büki A, Glocker B, Menon DK, Newcombe VFJ. Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study. EBioMedicine 2022; 75:103777. [PMID: 34959133 PMCID: PMC8718895 DOI: 10.1016/j.ebiom.2021.103777] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 11/12/2021] [Accepted: 12/10/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI. METHODS Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering. FINDINGS 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0·48-0·49; p<0·05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0·44-0·44; p<0·01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83·9% of variance, with phenotyping characteristics related to clinical injury severity. INTERPRETATION Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury. FUNDING CENTER-TBI is funded by the European Union 7th Framework programme (EC grant 602150).
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Affiliation(s)
- Daniel P Whitehouse
- University Division of Anaesthesia, Department of Medicine, University of Cambridge, UK
| | - Miguel Monteiro
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - Endre Czeiter
- Department of Neurosurgery, Medical School, University of Pécs, Rét u. 2, H-7623 Pécs, Hungary; Neurotrauma Research Group, Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, H-7624 Pécs, Hungary; MTA-PTE Clinical Neuroscience MR Research Group; Pécs, Hungary
| | - Thijs Vande Vyvere
- Research and Development, Icometrix, Leuven, Belgium; Department of Radiology, Antwerp University Hospital and University of Antwerp, Wilrijkstraat 10, 2650, Edegem, Belgium
| | - Fernanda Valerio
- University Division of Anaesthesia, Department of Medicine, University of Cambridge, UK
| | - Zheng Ye
- University Division of Anaesthesia, Department of Medicine, University of Cambridge, UK
| | - Krisztina Amrein
- Department of Neurosurgery, Medical School, University of Pécs, Rét u. 2, H-7623 Pécs, Hungary; Neurotrauma Research Group, Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, H-7624 Pécs, Hungary
| | | | - Haiyan Xu
- Program for Neurotrauma, Neuroproteomics and Biomarker Research, Departments of Emergency Medicine, Psychiatry and Neuroscience, University of Florida, McKnight Brain Institute, L4-100L 1149 South Newell Drive, Gainesville, FL 32611, USA
| | - Zhihui Yang
- Program for Neurotrauma, Neuroproteomics and Biomarker Research, Departments of Emergency Medicine, Psychiatry and Neuroscience, University of Florida, McKnight Brain Institute, L4-100L 1149 South Newell Drive, Gainesville, FL 32611, USA
| | - Jan Verheyden
- Research and Development, Icometrix, Leuven, Belgium
| | - Tilak Das
- Department of Radiology, Addenbrooke's Hospital, Cambridge, UK
| | | | - Ewout Steyerberg
- Center for Medical Decision Making, Department of Public Health, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Wijlrijkstraat 10, 2650 Edegem, Belgium
| | - Kevin K W Wang
- Program for Neurotrauma, Neuroproteomics and Biomarker Research, Departments of Emergency Medicine, Psychiatry and Neuroscience, University of Florida, McKnight Brain Institute, L4-100L 1149 South Newell Drive, Gainesville, FL 32611, USA; Brain Rehabilitation Research Center, Malcom Randall Veterans Affairs Medical Center (VAMC), 1601 SW, Archer Rd. Gainesville FL 32608, USA
| | - András Büki
- Department of Neurosurgery, Medical School, University of Pécs, Rét u. 2, H-7623 Pécs, Hungary; Neurotrauma Research Group, Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, H-7624 Pécs, Hungary
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - David K Menon
- University Division of Anaesthesia, Department of Medicine, University of Cambridge, UK
| | - Virginia F J Newcombe
- University Division of Anaesthesia, Department of Medicine, University of Cambridge, UK.
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7
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Li Z, Kamnitsas K, Glocker B. Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation. IEEE Trans Med Imaging 2021; 40:1065-1077. [PMID: 33351758 DOI: 10.1109/tmi.2020.3046692] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization. In this study, we provide new insights on the problem of overfitting under class imbalance by inspecting the network behavior. We find empirically that when training with limited data and strong class imbalance, at test time the distribution of logit activations may shift across the decision boundary, while samples of the well-represented class seem unaffected. This bias leads to a systematic under-segmentation of small structures. This phenomenon is consistently observed for different databases, tasks and network architectures. To tackle this problem, we introduce new asymmetric variants of popular loss functions and regularization techniques including a large margin loss, focal loss, adversarial training, mixup and data augmentation, which are explicitly designed to counter logit shift of the under-represented classes. Extensive experiments are conducted on several challenging segmentation tasks. Our results demonstrate that the proposed modifications to the objective function can lead to significantly improved segmentation accuracy compared to baselines and alternative approaches.
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8
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Mitchell JR, Kamnitsas K, Singleton KW, Whitmire SA, Clark-Swanson KR, Ranjbar S, Rickertsen CR, Johnston SK, Egan KM, Rollison DE, Arrington J, Krecke KN, Passe TJ, Verdoorn JT, Nagelschneider AA, Carr CM, Port JD, Patton A, Campeau NG, Liebo GB, Eckel LJ, Wood CP, Hunt CH, Vibhute P, Nelson KD, Hoxworth JM, Patel AC, Chong BW, Ross JS, Boxerman JL, Vogelbaum MA, Hu LS, Glocker B, Swanson KR. Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data. J Med Imaging (Bellingham) 2020; 7:055501. [PMID: 33102623 PMCID: PMC7567400 DOI: 10.1117/1.jmi.7.5.055501] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 09/21/2020] [Indexed: 11/17/2022] Open
Abstract
Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p<0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its “human teachers” and produced output that was better, on average, than its training data.
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Affiliation(s)
- Joseph Ross Mitchell
- H. Lee Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
| | | | - Kyle W Singleton
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States
| | - Scott A Whitmire
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States
| | | | - Sara Ranjbar
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States
| | | | - Sandra K Johnston
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States.,University of Washington, Department of Radiology, Seattle, Washington, United States
| | - Kathleen M Egan
- H. Lee Moffitt Cancer Center and Research Institute, Department of Cancer Epidemiology, Tampa, Florida, United States
| | - Dana E Rollison
- H. Lee Moffitt Cancer Center and Research Institute, Department of Cancer Epidemiology, Tampa, Florida, United States
| | - John Arrington
- H. Lee Moffitt Cancer Center and Research Institute, Department of Diagnostic Imaging and Interventional Radiology, Tampa, Florida, United States
| | - Karl N Krecke
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Theodore J Passe
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Jared T Verdoorn
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | - Carrie M Carr
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - John D Port
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Alice Patton
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Norbert G Campeau
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Greta B Liebo
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Laurence J Eckel
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Christopher P Wood
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Christopher H Hunt
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Prasanna Vibhute
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Kent D Nelson
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Joseph M Hoxworth
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Ameet C Patel
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Brian W Chong
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Jeffrey S Ross
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Jerrold L Boxerman
- Rhode Island Hospital and Alpert Medical School of Brown University, Department of Diagnostic Imaging, Providence, Rhode Island, United States
| | - Michael A Vogelbaum
- H. Lee Moffitt Cancer Center and Research Institute, Department of Neurosurgery, Tampa, Florida, United States
| | - Leland S Hu
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States.,Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Ben Glocker
- Imperial College, Biomedical Image Analysis Group, London, United Kingdom
| | - Kristin R Swanson
- Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona, United States.,Mayo Clinic, Department of Neurosurgery, Phoenix, Arizona, United States
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9
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Monteiro M, Newcombe VFJ, Mathieu F, Adatia K, Kamnitsas K, Ferrante E, Das T, Whitehouse D, Rueckert D, Menon DK, Glocker B. Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health 2020; 2:e314-e322. [PMID: 33328125 DOI: 10.1016/s2589-7500(20)30085-6] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/09/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types. METHODS Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification, lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was done on an independent set of 500 patients from India. FINDINGS 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres: 184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived lesion volumes showed a mean difference of 0·86 mL (95% CI -5·23 to 6·94) for intraparenchymal haemorrhage, 1·83 mL (-12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (-9·38 to 13·56) for perilesional oedema, and 0·07 mL (-1·00 to 1·13) for intraventricular haemorrhage. INTERPRETATION We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of lesion burden and progression, with potential applications for personalised treatment strategies and clinical research in TBI. FUNDING European Union 7th Framework Programme, Hannelore Kohl Stiftung, OneMind, NeuroTrauma Sciences, Integra Neurosciences, European Research Council Horizon 2020.
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Affiliation(s)
- Miguel Monteiro
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK.
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Francois Mathieu
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Krishma Adatia
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Konstantinos Kamnitsas
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Enzo Ferrante
- sinc(i), FICH-Universidad Nacional del Litoral, CONICET, Santa Fe, Argentina
| | - Tilak Das
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Daniel Whitehouse
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
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10
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Biffi C, Cerrolaza JJ, Tarroni G, Bai W, de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad SK, Cook SA, O'Regan DP, Rueckert D. Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. IEEE Trans Med Imaging 2020; 39:2088-2099. [PMID: 31944949 PMCID: PMC7269693 DOI: 10.1109/tmi.2020.2964499] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
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11
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Mathieu F, Zeiler FA, Ercole A, Monteiro M, Kamnitsas K, Glocker B, Whitehouse DP, Das T, Smielewski P, Czosnyka M, Hutchinson PJ, Newcombe VFJ, Menon DK. Relationship between Measures of Cerebrovascular Reactivity and Intracranial Lesion Progression in Acute Traumatic Brain Injury Patients: A CENTER-TBI Study. J Neurotrauma 2020; 37:1556-1565. [PMID: 31928143 DOI: 10.1089/neu.2019.6814] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Failure of cerebral autoregulation has been linked to unfavorable outcome after traumatic brain injury (TBI). Preliminary evidence from a small, retrospective, single-center analysis suggests that autoregulatory dysfunction may be associated with traumatic lesion expansion, particularly for pericontusional edema. The goal of this study was to further explore these associations using prospective, multi-center data from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) and to further explore the relationship between autoregulatory failure, lesion progression, and patient outcome. A total of 88 subjects from the CENTER-TBI High Resolution ICU Sub-Study cohort were included. All patients had an admission computed tomography (CT) scan and early repeat scan available, as well as high-frequency neurophysiological recordings covering the between-scan interval. Using a novel, semiautomated approach at lesion segmentation, we calculated absolute changes in volume of contusion core, pericontusional edema, and extra-axial hemorrhage between the imaging studies. We then evaluated associations between cerebrovascular reactivity metrics and radiological lesion progression using mixed-model regression. Analyses were adjusted for baseline covariates and non-neurophysiological factors associated with lesion growth using multi-variate methods. Impairment in cerebrovascular reactivity was significantly associated with progression of pericontusional edema and, to a lesser degree, intraparenchymal hemorrhage. In contrast, there were no significant associations with extra-axial hemorrhage. The strongest relationships were observed between RAC-based metrics and edema formation. Pulse amplitude index showed weaker, but consistent, associations with contusion growth. Cerebrovascular reactivity metrics remained strongly associated with lesion progression after taking into account contributions from non-neurophysiological factors and mean cerebral perfusion pressure. Total hemorrhagic core and edema volumes on repeat CT were significantly larger in patients who were deceased at 6 months, and the amount of edema was greater in patients with an unfavourable outcome (Glasgow Outcome Scale-Extended 1-4). Our study suggests associations between autoregulatory failure, traumatic edema progression, and poor outcome. This is in keeping with findings from a single-center retrospective analysis, providing multi-center prospective data to support those results.
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Affiliation(s)
- François Mathieu
- Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.,Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Frederick A Zeiler
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.,Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnibeg, Manitoba, Canada.,Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnibeg, Manitoba, Canada.,Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnibeg, Manitoba, Canada
| | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Miguel Monteiro
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | | | - Ben Glocker
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | | | - Tilak Das
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge, Cambridge, Cambridge, United Kingdom
| | - Peter Smielewski
- Brain Physics Laboratory, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, Cambridge, United Kingdom.,Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, Cambridge, United Kingdom.,Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland
| | - Peter J Hutchinson
- Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, Cambridge, United Kingdom
| | | | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
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12
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Wu O, Winzeck S, Giese AK, Hancock BL, Etherton MR, Bouts MJRJ, Donahue K, Schirmer MD, Irie RE, Mocking SJT, McIntosh EC, Bezerra R, Kamnitsas K, Frid P, Wasselius J, Cole JW, Xu H, Holmegaard L, Jiménez-Conde J, Lemmens R, Lorentzen E, McArdle PF, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Stanne TM, Thijs V, Vagal A, Woo D, Bevan S, Kittner SJ, Mitchell BD, Rosand J, Worrall BB, Jern C, Lindgren AG, Maguire J, Rost NS. Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data. Stroke 2019; 50:1734-1741. [PMID: 31177973 PMCID: PMC6728139 DOI: 10.1161/strokeaha.119.025373] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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Affiliation(s)
- Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Stefan Winzeck
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
- Division of Anaesthesia, Department of Medicine, University
of Cambridge, UK
| | - Anne-Katrin Giese
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
| | - Brandon L. Hancock
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Mark R. Etherton
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
| | - Mark J. R. J. Bouts
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Kathleen Donahue
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
| | - Markus D. Schirmer
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
| | - Robert E. Irie
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Elissa C. McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | - Raquel Bezerra
- Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital (MGH), 149 13th Street,
Charlestown, MA, USA
| | | | - Petrea Frid
- Department of Clinical Sciences Lund, Lund University,
Sweden
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Lund University,
Sweden
- Department of Radiology, Skåne University Hospital,
Lund, Sweden
| | - John W. Cole
- Department of Neurology, University of Maryland School of
Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
| | - Lukas Holmegaard
- Institute of Neuroscience and Physiology, the Sahlgrenska
Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jordi Jiménez-Conde
- Department of Neurology, Neurovascular Research Group
(NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions
Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of
Neurosciences, Experimental Neurology; VIB – Center for Brain & Disease
Research; University Hospitals Leuven, Department of Neurology, B-3000 Leuven,
Belgium
| | - Eric Lorentzen
- Department of Laboratory Medicine, Institute of
Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg,
Sweden
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
| | | | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group
(NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions
Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine,
University of Miami, Miami; The Evelyn F. McKnight Brain Institute; FL, USA
| | - Ralph L. Sacco
- Department of Neurology, Miller School of Medicine,
University of Miami, Miami; The Evelyn F. McKnight Brain Institute; FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of
Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway
University of London (ICR2UL), Egham, UK, Ashford and St Peter’s Hospital,
UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical
College, Krakow, Poland
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of
Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg,
Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and
Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg,
Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College
of Medicine, Cincinnati, OH, USA
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine,
University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Stephen Bevan
- School of Life Science, University of Lincoln, Lincoln,
UK
| | - Steven J. Kittner
- Department of Neurology, University of Maryland School of
Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Braxton D. Mitchell
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
- Geriatrics Research and Education Clinical Center,
Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Jonathan Rosand
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health
| | - Bradford B. Worrall
- Departments of Neurology and Public Health Sciences,
University of Virginia, Charlottesville, VA, USA
| | - Christina Jern
- Department of Laboratory Medicine, Institute of
Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg,
Sweden
| | - Arne G. Lindgren
- Department of Clinical Sciences Lund, Lund University,
Sweden
- Department of Neurology and Rehabilitation Medicine,
Neurology, Skåne University Hospital, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, Australia
| | - Natalia S. Rost
- JP Kistler Stroke Research Center, Department of Neurology,
MGH, Boston, MA, USA
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13
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Winzeck S, Mocking SJT, Bezerra R, Bouts MJRJ, McIntosh EC, Diwan I, Garg P, Chutinet A, Kimberly WT, Copen WA, Schaefer PW, Ay H, Singhal AB, Kamnitsas K, Glocker B, Sorensen AG, Wu O. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI. AJNR Am J Neuroradiol 2019; 40:938-945. [PMID: 31147354 DOI: 10.3174/ajnr.a6077] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 04/19/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. MATERIALS AND METHODS Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm3). RESULTS An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001). CONCLUSIONS Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.
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Affiliation(s)
- S Winzeck
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Division of Anaesthesia (S.W.), Department of Medicine, University of Cambridge, Cambridge, UK
| | - S J T Mocking
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - R Bezerra
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - M J R J Bouts
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - E C McIntosh
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - I Diwan
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - P Garg
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - A Chutinet
- Departments of Neurology (A.C., W.T.K., H.A., A.B.S.).,Department of Medicine (A.C.), Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - W T Kimberly
- Departments of Neurology (A.C., W.T.K., H.A., A.B.S.)
| | - W A Copen
- Radiology (W.A.C., P.W.S.), Massachusetts General Hospital, Boston, Massachusetts
| | - P W Schaefer
- Radiology (W.A.C., P.W.S.), Massachusetts General Hospital, Boston, Massachusetts
| | - H Ay
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Departments of Neurology (A.C., W.T.K., H.A., A.B.S.)
| | - A B Singhal
- Departments of Neurology (A.C., W.T.K., H.A., A.B.S.)
| | - K Kamnitsas
- Department of Computing (K.K., B.G.), Imperial College London, London, UK
| | - B Glocker
- Department of Computing (K.K., B.G.), Imperial College London, London, UK
| | - A G Sorensen
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - O Wu
- From the Department of Radiology (S.W., S.J.T.M., R.B., M.J.R.J.B., E.C.M., I.D., P.G., H.A., A.G.S., O.W.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
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Alansary A, Oktay O, Li Y, Folgoc LL, Hou B, Vaillant G, Kamnitsas K, Vlontzos A, Glocker B, Kainz B, Rueckert D. Evaluating reinforcement learning agents for anatomical landmark detection. Med Image Anal 2019; 53:156-164. [PMID: 30784956 PMCID: PMC7610752 DOI: 10.1016/j.media.2019.02.007] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 02/01/2019] [Accepted: 02/12/2019] [Indexed: 11/29/2022]
Abstract
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.
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Affiliation(s)
- Amir Alansary
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK.
| | - Ozan Oktay
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Yuanwei Li
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Loic Le Folgoc
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Benjamin Hou
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Ghislain Vaillant
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | | | - Athanasios Vlontzos
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Ben Glocker
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Bernhard Kainz
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK
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15
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Ouyang C, Kamnitsas K, Biffi C, Duan J, Rueckert D. Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation. Lecture Notes in Computer Science 2019. [DOI: 10.1007/978-3-030-32245-8_74] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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16
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Singleton K, Mitchell J, Ranjbar S, Kamnitsas K, Whitmire S, Clark-Swanson K, Rickertsen C, Rubin J, Glocker B, Hu L, Swanson K. NIMG-07. DEEP LEARNING DETECTS DIFFERENCES IN THE MRIs OF MALE AND FEMALE GLIOMAS. Neuro Oncol 2018. [DOI: 10.1093/neuonc/noy148.735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kyle Singleton
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, AZ, Phoenix, AZ, USA
| | | | - Sara Ranjbar
- Mathematical Neuro-Oncology Lab, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | | | | | | | | | - Joshua Rubin
- Washington University School of Medicine, St. Louis, MO, USA
| | - Ben Glocker
- Imperial College, London, England, United Kingdom
| | - Leland Hu
- Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Kristin Swanson
- Mathematical Neuro-Oncology Lab, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
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17
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Mitchell J, Kamnitsas K, Singleton K, Whitmire S, Clark-Swanson K, Rickertsen C, Glocker B, Hu L, Swanson K. NIMG-23. DEEP LEARNING FOR ACCURATE, RAPID, FULLY AUTOMATIC MEASUREMENT OF BRAIN TUMOR-ASSOCIATED ABNORMALITY SEEN ON MRI. Neuro Oncol 2018. [DOI: 10.1093/neuonc/noy148.749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
| | | | - Kyle Singleton
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ, Phoenix, AZ, USA
| | | | | | | | - Ben Glocker
- Imperial College, London, England, United Kingdom
| | - Leland Hu
- Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Kristin Swanson
- Mathematical Neuro-Oncology Lab, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
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18
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Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, de Marvao A, Dawes T, O'Regan DP, Kainz B, Glocker B, Rueckert D. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Trans Med Imaging 2018; 37:384-395. [PMID: 28961105 DOI: 10.1109/tmi.2017.2743464] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
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19
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Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D, Glocker B. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2018. [DOI: 10.1007/978-3-319-75238-9_38] [Citation(s) in RCA: 170] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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20
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Alansary A, Folgoc LL, Vaillant G, Oktay O, Li Y, Bai W, Passerat-Palmbach J, Guerrero R, Kamnitsas K, Hou B, McDonagh S, Glocker B, Kainz B, Rueckert D. Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 2018. [DOI: 10.1007/978-3-030-00928-1_32] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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21
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Maas AIR, Menon DK, Adelson PD, Andelic N, Bell MJ, Belli A, Bragge P, Brazinova A, Büki A, Chesnut RM, Citerio G, Coburn M, Cooper DJ, Crowder AT, Czeiter E, Czosnyka M, Diaz-Arrastia R, Dreier JP, Duhaime AC, Ercole A, van Essen TA, Feigin VL, Gao G, Giacino J, Gonzalez-Lara LE, Gruen RL, Gupta D, Hartings JA, Hill S, Jiang JY, Ketharanathan N, Kompanje EJO, Lanyon L, Laureys S, Lecky F, Levin H, Lingsma HF, Maegele M, Majdan M, Manley G, Marsteller J, Mascia L, McFadyen C, Mondello S, Newcombe V, Palotie A, Parizel PM, Peul W, Piercy J, Polinder S, Puybasset L, Rasmussen TE, Rossaint R, Smielewski P, Söderberg J, Stanworth SJ, Stein MB, von Steinbüchel N, Stewart W, Steyerberg EW, Stocchetti N, Synnot A, Te Ao B, Tenovuo O, Theadom A, Tibboel D, Videtta W, Wang KKW, Williams WH, Wilson L, Yaffe K, Adams H, Agnoletti V, Allanson J, Amrein K, Andaluz N, Anke A, Antoni A, van As AB, Audibert G, Azaševac A, Azouvi P, Azzolini ML, Baciu C, Badenes R, Barlow KM, Bartels R, Bauerfeind U, Beauchamp M, Beer D, Beer R, Belda FJ, Bellander BM, Bellier R, Benali H, Benard T, Beqiri V, Beretta L, Bernard F, Bertolini G, Bilotta F, Blaabjerg M, den Boogert H, Boutis K, Bouzat P, Brooks B, Brorsson C, Bullinger M, Burns E, Calappi E, Cameron P, Carise E, Castaño-León AM, Causin F, Chevallard G, Chieregato A, Christie B, Cnossen M, Coles J, Collett J, Della Corte F, Craig W, Csato G, Csomos A, Curry N, Dahyot-Fizelier C, Dawes H, DeMatteo C, Depreitere B, Dewey D, van Dijck J, Đilvesi Đ, Dippel D, Dizdarevic K, Donoghue E, Duek O, Dulière GL, Dzeko A, Eapen G, Emery CA, English S, Esser P, Ezer E, Fabricius M, Feng J, Fergusson D, Figaji A, Fleming J, Foks K, Francony G, Freedman S, Freo U, Frisvold SK, Gagnon I, Galanaud D, Gantner D, Giraud B, Glocker B, Golubovic J, Gómez López PA, Gordon WA, Gradisek P, Gravel J, Griesdale D, Grossi F, Haagsma JA, Håberg AK, Haitsma I, Van Hecke W, Helbok R, Helseth E, van Heugten C, Hoedemaekers C, Höfer S, Horton L, Hui J, Huijben JA, Hutchinson PJ, Jacobs B, van der Jagt M, Jankowski S, Janssens K, Jelaca B, Jones KM, Kamnitsas K, Kaps R, Karan M, Katila A, Kaukonen KM, De Keyser V, Kivisaari R, Kolias AG, Kolumbán B, Kolundžija K, Kondziella D, Koskinen LO, Kovács N, Kramer A, Kutsogiannis D, Kyprianou T, Lagares A, Lamontagne F, Latini R, Lauzier F, Lazar I, Ledig C, Lefering R, Legrand V, Levi L, Lightfoot R, Lozano A, MacDonald S, Major S, Manara A, Manhes P, Maréchal H, Martino C, Masala A, Masson S, Mattern J, McFadyen B, McMahon C, Meade M, Melegh B, Menovsky T, Moore L, Morgado Correia M, Morganti-Kossmann MC, Muehlan H, Mukherjee P, Murray L, van der Naalt J, Negru A, Nelson D, Nieboer D, Noirhomme Q, Nyirádi J, Oddo M, Okonkwo DO, Oldenbeuving AW, Ortolano F, Osmond M, Payen JF, Perlbarg V, Persona P, Pichon N, Piippo-Karjalainen A, Pili-Floury S, Pirinen M, Ple H, Poca MA, Posti J, Van Praag D, Ptito A, Radoi A, Ragauskas A, Raj R, Real RGL, Reed N, Rhodes J, Robertson C, Rocka S, Røe C, Røise O, Roks G, Rosand J, Rosenfeld JV, Rosenlund C, Rosenthal G, Rossi S, Rueckert D, de Ruiter GCW, Sacchi M, Sahakian BJ, Sahuquillo J, Sakowitz O, Salvato G, Sánchez-Porras R, Sándor J, Sangha G, Schäfer N, Schmidt S, Schneider KJ, Schnyer D, Schöhl H, Schoonman GG, Schou RF, Sir Ö, Skandsen T, Smeets D, Sorinola A, Stamatakis E, Stevanovic A, Stevens RD, Sundström N, Taccone FS, Takala R, Tanskanen P, Taylor MS, Telgmann R, Temkin N, Teodorani G, Thomas M, Tolias CM, Trapani T, Turgeon A, Vajkoczy P, Valadka AB, Valeinis E, Vallance S, Vámos Z, Vargiolu A, Vega E, Verheyden J, Vik A, Vilcinis R, Vleggeert-Lankamp C, Vogt L, Volovici V, Voormolen DC, Vulekovic P, Vande Vyvere T, Van Waesberghe J, Wessels L, Wildschut E, Williams G, Winkler MKL, Wolf S, Wood G, Xirouchaki N, Younsi A, Zaaroor M, Zelinkova V, Zemek R, Zumbo F. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 2017; 16:987-1048. [DOI: 10.1016/s1474-4422(17)30371-x] [Citation(s) in RCA: 822] [Impact Index Per Article: 117.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 07/06/2017] [Accepted: 09/27/2017] [Indexed: 12/11/2022]
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Ledig C, Kamnitsas K, Koikkalainen J, Posti JP, Takala RSK, Katila A, Frantzén J, Ala-Seppälä H, Kyllönen A, Maanpää HR, Tallus J, Lötjönen J, Glocker B, Tenovuo O, Rueckert D. Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging. PLoS One 2017; 12:e0188152. [PMID: 29182625 PMCID: PMC5705131 DOI: 10.1371/journal.pone.0188152] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 11/01/2017] [Indexed: 02/02/2023] Open
Abstract
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
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Affiliation(s)
- Christian Ledig
- Imperial College London, Department of Computing, London, United Kingdom
- * E-mail:
| | | | - Juha Koikkalainen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Jussi P. Posti
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Riikka S. K. Takala
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Ari Katila
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Janek Frantzén
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Henna Ala-Seppälä
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Anna Kyllönen
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | | | - Jussi Tallus
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jyrki Lötjönen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Ben Glocker
- Imperial College London, Department of Computing, London, United Kingdom
| | - Olli Tenovuo
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
| | - Daniel Rueckert
- Imperial College London, Department of Computing, London, United Kingdom
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Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert D. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound. IEEE Trans Med Imaging 2017; 36:2204-2215. [PMID: 28708546 PMCID: PMC6051487 DOI: 10.1109/tmi.2017.2712367] [Citation(s) in RCA: 135] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 06/01/2017] [Accepted: 06/01/2017] [Indexed: 05/22/2023]
Abstract
Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.
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Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AG. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach. Med Phys 2017; 44:5210-5220. [PMID: 28756622 DOI: 10.1002/mp.12492] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 06/05/2017] [Accepted: 07/09/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. METHODS The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. CONCLUSIONS Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.
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Affiliation(s)
- Ioannis Lavdas
- Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London, W12 0NN, UK
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, Huxley Building, 180 Queen's Gate, London, SW7 2AZ, UK
| | - Konstantinos Kamnitsas
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, Huxley Building, 180 Queen's Gate, London, SW7 2AZ, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, Huxley Building, 180 Queen's Gate, London, SW7 2AZ, UK
| | - Henrietta Mair
- Department of Imaging, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Amandeep Sandhu
- Department of Radiology Hammersmith Hospital, Imperial College Healthcare NHS Trust, DuCane Road, London, W12 0NN, UK
| | - Stuart A Taylor
- Department of Imaging, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Eric O Aboagye
- Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London, W12 0NN, UK
| | - Andrea G Rockall
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
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Valindria VV, Lavdas I, Bai W, Kamnitsas K, Aboagye EO, Rockall AG, Rueckert D, Glocker B. Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth. IEEE Trans Med Imaging 2017; 36:1597-1606. [PMID: 28436849 DOI: 10.1109/tmi.2017.2665165] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.
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Ciller C, De Zanet S, Kamnitsas K, Maeder P, Glocker B, Munier FL, Rueckert D, Thiran JP, Bach Cuadra M, Sznitman R. Multi-channel MRI segmentation of eye structures and tumors using patient-specific features. PLoS One 2017; 12:e0173900. [PMID: 28350816 PMCID: PMC5369682 DOI: 10.1371/journal.pone.0173900] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 02/28/2017] [Indexed: 02/03/2023] Open
Abstract
Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.
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Affiliation(s)
- Carlos Ciller
- Radiology Department, CIBM, Lausanne University and University Hospital, Lausanne, Switzerland
- Ophthalmic Technology Group, ARTORG Center Univ. of Bern, Bern, Switzerland
- * E-mail:
| | - Sandro De Zanet
- Ophthalmic Technology Group, ARTORG Center Univ. of Bern, Bern, Switzerland
| | | | - Philippe Maeder
- Radiology Department, CIBM, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ben Glocker
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Francis L. Munier
- Unit of Pediatric Ocular Oncology, Jules Gonin Eye Hospital, Lausanne, Switzerland
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Jean-Philippe Thiran
- Radiology Department, CIBM, Lausanne University and University Hospital, Lausanne, Switzerland
- Signal Processing Laboratory, Ećole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Radiology Department, CIBM, Lausanne University and University Hospital, Lausanne, Switzerland
- Signal Processing Laboratory, Ećole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Raphael Sznitman
- Ophthalmic Technology Group, ARTORG Center Univ. of Bern, Bern, Switzerland
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Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, Rueckert D. DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. IEEE Trans Med Imaging 2017; 36:674-683. [PMID: 27845654 PMCID: PMC7115996 DOI: 10.1109/tmi.2016.2621185] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
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McDonagh S, Hou B, Alansary A, Oktay O, Kamnitsas K, Rutherford M, Hajnal JV, Kainz B. Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging. Lecture Notes in Computer Science 2017. [DOI: 10.1007/978-3-319-67564-0_12] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Maier O, Menze BH, von der Gablentz J, Ḧani L, Heinrich MP, Liebrand M, Winzeck S, Basit A, Bentley P, Chen L, Christiaens D, Dutil F, Egger K, Feng C, Glocker B, Götz M, Haeck T, Halme HL, Havaei M, Iftekharuddin KM, Jodoin PM, Kamnitsas K, Kellner E, Korvenoja A, Larochelle H, Ledig C, Lee JH, Maes F, Mahmood Q, Maier-Hein KH, McKinley R, Muschelli J, Pal C, Pei L, Rangarajan JR, Reza SMS, Robben D, Rueckert D, Salli E, Suetens P, Wang CW, Wilms M, Kirschke JS, Kr Amer UM, Münte TF, Schramm P, Wiest R, Handels H, Reyes M. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 2016; 35:250-269. [PMID: 27475911 DOI: 10.1016/j.media.2016.07.009] [Citation(s) in RCA: 214] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 05/30/2016] [Accepted: 07/20/2016] [Indexed: 01/14/2023]
Abstract
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
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Affiliation(s)
- Oskar Maier
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Live Science, University of Lübeck, Germany
| | - Bjoern H Menze
- Institute for Advanced Study and Department of Computer Science, Technische Universität München, Munich, Germany
| | | | - Levin Ḧani
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | | | | | - Stefan Winzeck
- Institute for Advanced Study and Department of Computer Science, Technische Universität München, Munich, Germany
| | - Abdul Basit
- Pakistan Institute of Nuclear Science and Technology, Islamabad, Pakistan
| | - Paul Bentley
- Division of Brain Sciences, Department of Medicine, Imperial College London, UK
| | - Liang Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
- Division of Brain Sciences, Department of Medicine, Imperial College London, UK
| | - Daan Christiaens
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | | | - Karl Egger
- Department of Neuroradiology, University Medical Center Freiburg, Germany
| | - Chaolu Feng
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Michael Götz
- Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Tom Haeck
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Hanna-Leena Halme
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering NBE, Aalto University School of Science, Aalto, Finland
| | | | - Khan M Iftekharuddin
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | | | | | - Elias Kellner
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany
| | - Antti Korvenoja
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | - Christian Ledig
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Jia-Hong Lee
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Frederik Maes
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Qaiser Mahmood
- Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden
- Pakistan Institute of Nuclear Science and Technology, Islamabad, Pakistan
| | - Klaus H Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Richard McKinley
- Department of Diagnostic and Interventional Neuroradiology, Inselspital Bern, Switzerland
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chris Pal
- Ecole Polytechnique de Montréal, Canada
| | - Linmin Pei
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - Janaki Raman Rangarajan
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Syed M S Reza
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - David Robben
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Eero Salli
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Paul Suetens
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Ching-Wei Wang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Matthias Wilms
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Ulrike M Kr Amer
- Department of Neurology, University of Lübeck, Germany
- Institute of Psychology II, University of Lübeck, Germany
| | | | - Peter Schramm
- Institute of Neuroradiology, University Medical Center Lübeck
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, Inselspital Bern, Switzerland
| | - Heinz Handels
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Mauricio Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
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Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Rueckert D, Glocker B. DeepMedic for Brain Tumor Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2016. [DOI: 10.1007/978-3-319-55524-9_14] [Citation(s) in RCA: 127] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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