1
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Gillot M, Miranda F, Baquero B, Ruellas A, Gurgel M, Al Turkestani N, Anchling L, Hutin N, Biggs E, Yatabe M, Paniagua B, Fillion-Robin JC, Allemang D, Bianchi J, Cevidanes L, Prieto JC. Automatic landmark identification in cone-beam computed tomography. Orthod Craniofac Res 2023; 26:560-567. [PMID: 36811276 PMCID: PMC10440369 DOI: 10.1111/ocr.12642] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 09/19/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/24/2023]
Abstract
OBJECTIVE To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. MATERIALS AND METHODS One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. RESULTS Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. CONCLUSION The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
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Affiliation(s)
- Maxime Gillot
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- CPE Lyon, Lyon, France
| | - Felicia Miranda
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- Department of Orthodontics, Bauru Dental School, University of São Paulo, Bauru, Brazil
| | - Baptiste Baquero
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- CPE Lyon, Lyon, France
| | - Antonio Ruellas
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
| | - Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Luc Anchling
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- CPE Lyon, Lyon, France
| | - Nathan Hutin
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
- CPE Lyon, Lyon, France
| | - Elizabeth Biggs
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
| | - Marilia Yatabe
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
| | | | | | | | - Jonas Bianchi
- Department of Orthodontics, University of the Pacific, San Francisco, CA, USA
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, MI, Ann Arbor, USA
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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2
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Vicory J, Han Y, Prieto JC, Allemang D, Leclercq M, Bowley C, Scheirich H, Fillion-Robin JC, Pizer S, Fishbaugh J, Gerig G, Styner M, Paniagua B. SlicerSALT: From Medical Images to Quantitative Insights of Anatomy. Shape Med Imaging (2023) 2023; 14350:201-210. [PMID: 38250732 PMCID: PMC10798161 DOI: 10.1007/978-3-031-46914-5_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Three-dimensional (3D) shape lies at the core of understanding the physical objects that surround us. In the biomedical field, shape analysis has been shown to be powerful in quantifying how anatomy changes with time and disease. The Shape AnaLysis Toolbox (SALT) was created as a vehicle for disseminating advanced shape methodology as an open source, free, and comprehensive software tool. We present new developments in our shape analysis software package, including easy-to-interpret statistical methods to better leverage the quantitative information contained in SALT's shape representations. We also show SlicerPipelines, a module to improve the usability of SALT by facilitating the analysis of large-scale data sets, automating workflows for non-expert users, and allowing the distribution of reproducible workflows.
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Affiliation(s)
| | - Ye Han
- Kitware Inc, Carrboro, NC 27510, USA
| | | | | | | | | | | | | | - Steve Pizer
- University of North Carolina, Chapel Hill, NC 27599, USA
| | | | - Guido Gerig
- New York University, Brooklyn, NY 11201, USA
| | - Martin Styner
- University of North Carolina, Chapel Hill, NC 27599, USA
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3
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Hawrylycz M, Martone ME, Ascoli GA, Bjaalie JG, Dong HW, Ghosh SS, Gillis J, Hertzano R, Haynor DR, Hof PR, Kim Y, Lein E, Liu Y, Miller JA, Mitra PP, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray PL, Sanchez R, Regev A, Ropelewski A, Scheuermann RH, Tan SZK, Thompson CL, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey TL, Baker C, Baker KS, Baker PM, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D’Orazi F, Degatano K, Dichter B, Ding SL, Dolbeare T, Ecker JR, Fang R, Fillion-Robin JC, Fliss TP, Gee J, Gillespie T, Gouwens N, Zhang GQ, Halchenko YO, Harris NL, Herb BR, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan EA, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews KL, Mezias C, Miller MI, Mollenkopf T, Mufti S, Mungall CJ, Orvis J, Puchades MA, Qu L, Receveur JP, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven CTJ, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang YR, Zheng WJ, Zingg B. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biol 2023; 21:e3002133. [PMID: 37390046 PMCID: PMC10313015 DOI: 10.1371/journal.pbio.3002133] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2023] Open
Abstract
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
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Affiliation(s)
- Michael Hawrylycz
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Maryann E. Martone
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
- San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, Volgenau School of Engineering, George Mason University, Fairfax, Virginia, United States of America
| | - Jan G. Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Jesse Gillis
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Ronna Hertzano
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David R. Haynor
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, United States of America
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yufeng Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Jeremy A. Miller
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Partha P. Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Eran Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States of America
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Hanchuan Peng
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Patrick L. Ray
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Raymond Sanchez
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Aviv Regev
- Genentech, South San Francisco, California, United States of America
| | - Alex Ropelewski
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | | | - Shawn Zheng Kai Tan
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Carol L. Thompson
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Timothy Tickle
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hagen Tilgner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, United States of America
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Owen White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Aevermann
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - David Allemang
- Kitware Inc., Albany, New York, United States of America
| | - Seth Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas L. Athey
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Cody Baker
- CatalystNeuro, Benicia, California, United States of America
| | - Katherine S. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Pamela M. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Anita Bandrowski
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Samik Banerjee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Prajal Bishwakarma
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ambrose Carr
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Min Chen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Roni Choudhury
- Kitware Inc., Albany, New York, United States of America
| | - Jonah Cool
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Heather Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Florence D’Orazi
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Kylee Degatano
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | | | - Timothy P. Fliss
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - James Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Tom Gillespie
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Nathan Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Guo-Qiang Zhang
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hannover, New Hampshire, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Brian R. Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Gregory Hood
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Sam Horvath
- Kitware Inc., Albany, New York, United States of America
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Dorota Jarecka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Shengdian Jiang
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Farzaneh Khajouei
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elizabeth A. Kiernan
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Huseyin Kir
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Boudewijn Lelieveldt
- Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yang Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
| | - Hanqing Liu
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Lijuan Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Anup Markuhar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - James Mathews
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Kaylee L. Mathews
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Chris Mezias
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Michael I. Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Tyler Mollenkopf
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Maja A. Puchades
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Lei Qu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Joseph P. Receveur
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
- Ludwig Institute for Cancer Research, La Jolla, California, United States of America
| | - Nathan Sjoquist
- Microsoft Corporation, Seattle, Washington, United States of America
| | - Brian Staats
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Tward
- UCLA Brain Mapping Center, University of California, Los Angeles, California, United States of America
| | | | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Fangming Xie
- Department of Chemistry and Biochemistry, University of California Los Angeles, California, United States of America
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Zhixi Yun
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Yun Renee Zhang
- J. Craig Venter Institute, La Jolla, California, United States of America
| | - W. Jim Zheng
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Brian Zingg
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
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4
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Gillot M, Baquero B, Le C, Deleat-Besson R, Bianchi J, Ruellas A, Gurgel M, Yatabe M, Al Turkestani N, Najarian K, Soroushmehr R, Pieper S, Kikinis R, Paniagua B, Gryak J, Ioshida M, Massaro C, Gomes L, Oh H, Evangelista K, Chaves Junior CM, Garib D, Costa F, Benavides E, Soki F, Fillion-Robin JC, Joshi H, Cevidanes L, Prieto JC. Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR. PLoS One 2022; 17:e0275033. [PMID: 36223330 PMCID: PMC9555672 DOI: 10.1371/journal.pone.0275033] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [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: 05/10/2022] [Accepted: 09/09/2022] [Indexed: 11/06/2022] Open
Abstract
The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.
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Affiliation(s)
- Maxime Gillot
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
- * E-mail:
| | - Baptiste Baquero
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Celia Le
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Romain Deleat-Besson
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Jonas Bianchi
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Antonio Ruellas
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marcela Gurgel
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marilia Yatabe
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Najla Al Turkestani
- University of Michigan, Ann Arbor, Michigan, United States of America
- King Abdulaziz University, Jeddah, Saudi Arabia
| | - Kayvan Najarian
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Reza Soroushmehr
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Steve Pieper
- ISOMICS, Cambridge, Massachusetts, United States of America
| | - Ron Kikinis
- Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Jonathan Gryak
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marcos Ioshida
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Camila Massaro
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Liliane Gomes
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Heesoo Oh
- University of Pacific, Stockton, California, United States of America
| | | | | | | | - Fábio Costa
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Erika Benavides
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Fabiana Soki
- University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Hina Joshi
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Lucia Cevidanes
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Juan Carlos Prieto
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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5
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Léger É, Horvath S, Fillion-Robin JC, Allemang D, Gerber S, Juvekar P, Torio E, Kapur T, Pieper S, Pujol S, Bardsley R, Frisken S, Golby A. NousNav: A low-cost neuronavigation system for deployment in lower-resource settings. Int J Comput Assist Radiol Surg 2022; 17:1745-1750. [PMID: 35511395 DOI: 10.1007/s11548-022-02644-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Received: 01/11/2022] [Accepted: 04/14/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE NousNav is a complete low-cost neuronavigation system that aims to democratize access to higher-quality healthcare in lower-resource settings. NousNav's goal is to provide a model for local actors to be able to reproduce, build and operate a fully functional neuronavigation system at an affordable cost. METHODS NousNav is entirely open source and relies on low-cost off-the-shelf components, which makes it easy to reproduce and deploy in any region. NousNav's software is also specifically devised with the low-resource setting in mind. RESULTS It offers means for intuitive intraoperative control. The designed interface is also clean and simple. This allows for easy intraoperative use by either the practicing clinician or a nurse. It thus alleviates the need for a dedicated technician for operation. CONCLUSION A prototype implementation of the design was built. Hardware and algorithms were designed for robustness, ruggedness, modularity, to be standalone and data-agnostic. The built prototype demonstrates feasibility of the objectives.
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Affiliation(s)
- Étienne Léger
- Brigham and Women's Hospital, Boston, MA, USA. .,Harvard Medical School, Boston, MA, USA.
| | | | | | | | | | - Parikshit Juvekar
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Erickson Torio
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | | | - Sonia Pujol
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | | | - Sarah Frisken
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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6
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Ye Y, Barapatre S, Davis MK, Elliston KO, Davatzikos C, Fedorov A, Fillion-Robin JC, Foster I, Gilbertson JR, Lasso A, Miller JV, Morgan M, Pieper S, Raumann BE, Sarachan BD, Savova G, Silverstein JC, Taylor DP, Zelnis JB, Zhang GQ, Cuticchia J, Becich MJ. Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group. J Med Internet Res 2021; 23:e20028. [PMID: 34860667 PMCID: PMC8686402 DOI: 10.2196/20028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 12/14/2020] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background The National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019. Objective The charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective. Methods This paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene. Results Information available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS. Conclusions We recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene.
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Affiliation(s)
- Ye Ye
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seemran Barapatre
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Michael K Davis
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Keith O Elliston
- Axiomedix, Inc., Bedford, MA, United States.,PHEMI Systems Corp., Vancouver, BC, Canada.,tranSMART foundation, Wakefield, MA, United States
| | - Christos Davatzikos
- Department of Radiology, School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Andrey Fedorov
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Ian Foster
- Department of Computer Science, University of Chicago, Chicago, IL, United States
| | - John R Gilbertson
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Andras Lasso
- The Perk Lab for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada
| | | | - Martin Morgan
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | | | | | | | - Guergana Savova
- Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Donald P Taylor
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Joyce B Zelnis
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Guo-Qiang Zhang
- The University of Texas Health Science Center at Houston, Houston, TX, United States
| | | | - Michael J Becich
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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7
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Deleat-Besson R, Le C, Al Turkestani N, Zhang W, Dumont M, Brosset S, Carlos Prieto J, Cevidanes L, Bianchi J, Ruellas A, Gurgel M, Massaro C, Aliaga-Del Castillo A, Ioshida M, Yatabe M, Benavides E, Rios H, Soki F, Neiva G, Fernando Aristizabal J, Rey D, Antonia Alvarez M, Najarian K, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Soroushmehr R. Automatic Segmentation of Dental Root Canal and Merging with Crown Shape. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2948-2951. [PMID: 34891863 DOI: 10.1109/embc46164.2021.9630750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth. The merging algorithm then allows users to integrate dental models for quantitative assessments. Precision in dentistry has been mainly driven by dental crown surface characteristics, but information on tooth root morphology and position is important for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. In this paper we propose a patient specific classification and prediction of dental root canal and crown shape analysis workflow that employs image processing and machine learning methods to analyze crown surfaces, obtained by intraoral scanners, and three-dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography (CBCT).
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8
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Le C, Deleat-Besson R, Prieto J, Brosset S, Dumont M, Zhang W, Cevidanes L, Bianchi J, Ruellas A, Gomes L, Gurgel M, Massaro C, Aliaga-Del Castillo A, Yatabe M, Benavides E, Soki F, Al Turkestani N, Evangelista K, Goncalves J, Valladares-Neto J, Alves Garcia Silva M, Chaves C, Costa F, Garib D, Oh H, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Najarian K, Soroushmehr R. Automatic Segmentation of Mandibular Ramus and Condyles. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2952-2955. [PMID: 34891864 PMCID: PMC8994041 DOI: 10.1109/embc46164.2021.9630727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10-5. The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.
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9
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Fedorov A, Beichel R, Kalpathy-Cramer J, Clunie D, Onken M, Riesmeier J, Herz C, Bauer C, Beers A, Fillion-Robin JC, Lasso A, Pinter C, Pieper S, Nolden M, Maier-Hein K, Herrmann MD, Saltz J, Prior F, Fennessy F, Buatti J, Kikinis R. Quantitative Imaging Informatics for Cancer Research. JCO Clin Cancer Inform 2021; 4:444-453. [PMID: 32392097 PMCID: PMC7265794 DOI: 10.1200/cci.19.00165] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.
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Affiliation(s)
- Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | - Christian Herz
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | - Marco Nolden
- German Cancer Research Center, Heidelberg, Germany
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Fiona Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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10
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Al Turkestani N, Bianchi J, Deleat-Besson R, Le C, Tengfei L, Prieto JC, Gurgel M, Ruellas ACO, Massaro C, Aliaga Del Castillo A, Evangelista K, Yatabe M, Benavides E, Soki F, Zhang W, Najarian K, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Soroushmehr R, Cevidanes LHS. Clinical decision support systems in orthodontics: A narrative review of data science approaches. Orthod Craniofac Res 2021; 24 Suppl 2:26-36. [PMID: 33973362 DOI: 10.1111/ocr.12492] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/27/2022]
Abstract
Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.
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Affiliation(s)
- Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jonas Bianchi
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA, USA
| | - Romain Deleat-Besson
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Celia Le
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Li Tengfei
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Antonio C O Ruellas
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, School of Dentistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Camila Massaro
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Bauru Dental School, University of São Paulo, São Paulo, Brazil
| | - Aron Aliaga Del Castillo
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Bauru Dental School, University of São Paulo, São Paulo, Brazil
| | - Karine Evangelista
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, School of Dentistry, University of Goias, Goiania, Brazil
| | - Marilia Yatabe
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Erika Benavides
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Fabiana Soki
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Winston Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Martin Styner
- Departments Psychiatry and Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Lucia H S Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
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11
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Dumont M, Prieto JC, Brosset S, Cevidanes L, Bianchi J, Ruellas A, Gurgel M, Massaro C, Castillo AAD, Ioshida M, Yatabe M, Benavides E, Rios H, Soki F, Neiva G, Aristizabal JF, Rey D, Alvarez MA, Najarian K, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Soroushmehr R. Patient Specific Classification of Dental Root Canal and Crown Shape. Shape Med Imaging (2020) 2020; 12474:145-153. [PMID: 33385170 DOI: 10.1007/978-3-030-61056-2_12] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. An innovative approach is to use image processing and machine learning to combine crown surfaces, obtained by intraoral scanners, with three dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography. In this paper, we propose a patient specific classification of dental root canal and crown shape analysis workflow that is widely applicable.
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Affiliation(s)
- Maxime Dumont
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | | | - Serge Brosset
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Lucia Cevidanes
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Jonas Bianchi
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Antonio Ruellas
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Marcela Gurgel
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Camila Massaro
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | | | - Marcos Ioshida
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Marilia Yatabe
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Erika Benavides
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Hector Rios
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Fabiana Soki
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Gisele Neiva
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | | | | | | | - Kayvan Najarian
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | - Jonathan Gryak
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
| | | | | | | | - Reza Soroushmehr
- University of Michigan, 1011 North University Ave., Ann Arbor, MI 48109, USA
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12
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Bianchi J, Paniagua B, De Oliveira Ruellas AC, Fillion-Robin JC, Prietro JC, Gonçalves JR, Hoctor J, Yatabe M, Styner M, Li T, Gurgel ML, Chaves CM, Massaro C, Garib DG, Vilanova L, Castanha Henriques JF, Aliaga-Del Castillo A, Janson G, Iwasaki LR, Nickel JC, Evangelista K, Cevidanes L. 3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research. Multimodal Learn Clin Decis Support Clin Image Based Proc (2020) 2020; 12445:44-53. [PMID: 33415323 DOI: 10.1007/978-3-030-60946-7_5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.
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Affiliation(s)
- Jonas Bianchi
- University of Michigan, 1011 North University Ave, Ann Arbor, MI 48109, USA
| | | | | | | | - Juan C Prietro
- University of North Carolina, Chapel Hill, NC 27599, USA
| | | | | | - Marília Yatabe
- University of Michigan, 1011 North University Ave, Ann Arbor, MI 48109, USA
| | - Martin Styner
- University of North Carolina, Chapel Hill, NC 27599, USA
| | - TengFei Li
- University of North Carolina, Chapel Hill, NC 27599, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Lucia Cevidanes
- University of Michigan, 1011 North University Ave, Ann Arbor, MI 48109, USA
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13
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Pinter C, Lasso A, Choueib S, Asselin M, Fillion-Robin JC, Vimort JB, Martin K, Jolley MA, Fichtinger G. SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality. ACTA ACUST UNITED AC 2020; 2:108-117. [PMID: 33748693 DOI: 10.1109/tmrb.2020.2983199] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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: 12/27/2022]
Abstract
Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.
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Affiliation(s)
- Csaba Pinter
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | - Saleh Choueib
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | - Mark Asselin
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | | | | | - Ken Martin
- Kitware Incorporated, Carrboro, North Carolina, USA
| | | | - Gabor Fichtinger
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
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14
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Vicory J, Pascal L, Hernandez P, Fishbaugh J, Prieto J, Mostapha M, Huang C, Shah H, Hong J, Liu Z, Michoud L, Fillion-Robin JC, Gerig G, Zhu H, Pizer SM, Styner M, Paniagua B. SlicerSALT: Shape AnaLysis Toolbox. Shape Med Imaging (2018) 2018; 11167:65-72. [PMID: 31032495 PMCID: PMC6482453 DOI: 10.1007/978-3-030-04747-4_6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
SlicerSALT is an open-source platform for disseminating state-of-the-art methods for performing statistical shape analysis. These methods are developed as 3D Slicer extensions to take advantage of its powerful underlying libraries. SlicerSALT itself is a heavily customized 3D Slicer package that is designed to be easy to use for shape analysis researchers. The packaged methods include powerful techniques for creating and visualizing shape representations as well as performing various types of analysis.
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Affiliation(s)
| | | | | | | | | | | | - Chao Huang
- University of North Carolina at Chapel Hill
| | | | | | | | | | | | | | - Hongtu Zhu
- University of North Carolina at Chapel Hill
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15
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Lasso A, Nam HH, Dinh PV, Pinter C, Fillion-Robin JC, Pieper S, Jhaveri S, Vimort JB, Martin K, Asselin M, McGowan FX, Kikinis R, Fichtinger G, Jolley MA. Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality. J Am Soc Echocardiogr 2018; 31:1158-1160. [PMID: 30093145 DOI: 10.1016/j.echo.2018.06.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Indexed: 11/18/2022]
Affiliation(s)
- Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Ontario, Canada
| | - Hannah H Nam
- Department of Anesthesiology and Critical Care Medicine, Children' Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Patrick V Dinh
- Department of Anesthesiology and Critical Care Medicine, Children' Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Csaba Pinter
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Ontario, Canada
| | | | | | | | | | | | - Mark Asselin
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Ontario, Canada
| | - Francis X McGowan
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Medical Image Computing, University of Bremen, Bremen, Germany; Fraunhofer MEVIS, Bremen, Germany
| | - Gabor Fichtinger
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Ontario, Canada
| | - Matthew A Jolley
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Division of Cardiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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16
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Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Res 2017; 77:e87-e90. [PMID: 29092948 DOI: 10.1158/0008-5472.can-17-0336] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 04/26/2017] [Accepted: 06/23/2017] [Indexed: 11/16/2022]
Abstract
Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi Cancer Res; 77(21); e87-90. ©2017 AACR.
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Affiliation(s)
- Christian Herz
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | | | | | | | - Andras Lasso
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Csaba Pinter
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Gabor Fichtinger
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Ontario, Canada
| | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Department of Computer Science, University of Bremen, Bremen, Germany
- Fraunhofer MEVIS, Bremen, Germany
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts.
- Harvard Medical School, Harvard University, Boston, Massachusetts
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van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 2017; 77:e104-e107. [PMID: 29092951 DOI: 10.1158/0008-5472.can-17-0339] [Citation(s) in RCA: 2856] [Impact Index Per Article: 408.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 03/20/2017] [Accepted: 07/11/2017] [Indexed: 11/16/2022]
Abstract
Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.
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Affiliation(s)
- Joost J M van Griethuysen
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands.,GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Nicole Aucoin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Vivek Narayan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Regina G H Beets-Tan
- Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands.,GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. .,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select (case when (4896=2209) then null else ctxsys.drithsx.sn(1,4896) end) from dual) is null-- sxuy] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select (case when (1792=1792) then null else ctxsys.drithsx.sn(1,1792) end) from dual) is null] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 1553=1080-- bart] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 3178=convert(int,(select char(113)+char(118)+char(107)+char(106)+char(113)+(select (case when (3178=3178) then char(49) else char(48) end))+char(113)+char(113)+char(122)+char(118)+char(113)))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 2640=(select (case when (2640=6544) then 2640 else (select 6544 union select 6520) end))-- mzfc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [PMID: 22770690 DOI: 10.1016/j.mri.2012.05.001.3d] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 rlike (select (case when (4371=9904) then 0x31302e313031362f6a2e6d72692e323031322e30352e303031 else 0x28 end))-- qcki] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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25
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 5488=utl_inaddr.get_host_address(chr(113)||chr(118)||chr(107)||chr(106)||chr(113)||(select (case when (5488=5488) then 1 else 0 end) from dual)||chr(113)||chr(113)||chr(122)||chr(118)||chr(113))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 rlike (select (case when (5886=7226) then 0x31302e313031362f6a2e6d72692e323031322e30352e303031 else 0x28 end))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 or (select 7448 from(select count(*),concat(0x71766b6a71,(select (elt(7448=7448,1))),0x71717a7671,floor(rand(0)*2))x from information_schema.plugins group by x)a)-- dbin] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 or extractvalue(4152,concat(0x5c,0x71766b6a71,(select (elt(4152=4152,1))),0x71717a7671))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 order by 1-- xuuy] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 order by 1#] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and row(7715,4973)>(select count(*),concat(0x71766b6a71,(select (elt(7715=7715,1))),0x71717a7671,floor(rand(0)*2))x from (select 5924 union select 5845 union select 5797 union select 4165)a group by x)-- fnxo] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and extractvalue(9179,concat(0x5c,0x71766b6a71,(select (elt(9179=9179,1))),0x71717a7671))-- shgb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select (case when (2349=2349) then null else cast((chr(103)||chr(81)||chr(74)||chr(66)) as numeric) end)) is null-- zhfv] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select (case when (1792=1792) then null else ctxsys.drithsx.sn(1,1792) end) from dual) is null-- zbwn] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 rlike (select (case when (7990=7990) then 0x31302e313031362f6a2e6d72692e323031322e30352e303031 else 0x28 end))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select 8334 from(select count(*),concat(0x71766b6a71,(select (elt(8334=8334,1))),0x71717a7671,floor(rand(0)*2))x from information_schema.plugins group by x)a)-- nctr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and extractvalue(9179,concat(0x5c,0x71766b6a71,(select (elt(9179=9179,1))),0x71717a7671))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 3348=concat(char(113)+char(118)+char(107)+char(106)+char(113),(select (case when (3348=3348) then char(49) else char(48) end)),char(113)+char(113)+char(122)+char(118)+char(113))] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 2959=cast((chr(113)||chr(118)||chr(107)||chr(106)||chr(113))||(select (case when (2959=2959) then 1 else 0 end))::text||(chr(113)||chr(113)||chr(122)||chr(118)||chr(113)) as numeric)-- vwyg] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 3178=convert(int,(select char(113)+char(118)+char(107)+char(106)+char(113)+(select (case when (3178=3178) then char(49) else char(48) end))+char(113)+char(113)+char(122)+char(118)+char(113)))-- qyxx] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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41
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select 8334 from(select count(*),concat(0x71766b6a71,(select (elt(8334=8334,1))),0x71717a7671,floor(rand(0)*2))x from information_schema.plugins group by x)a)] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 or row(3442,7723)>(select count(*),concat(0x71766b6a71,(select (elt(3442=3442,1))),0x71717a7671,floor(rand(0)*2))x from (select 9605 union select 3910 union select 3326 union select 1181)a group by x)] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012; 30:1323-41. [PMID: 22770690 PMCID: PMC3466397 DOI: 10.1016/j.mri.2012.05.001] [Citation(s) in RCA: 3995] [Impact Index Per Article: 332.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Revised: 04/26/2012] [Accepted: 05/29/2012] [Indexed: 02/06/2023]
Abstract
Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and (select (case when (2349=2349) then null else cast((chr(103)||chr(81)||chr(74)||chr(66)) as numeric) end)) is null] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 5488=utl_inaddr.get_host_address(chr(113)||chr(118)||chr(107)||chr(106)||chr(113)||(select (case when (5488=5488) then 1 else 0 end) from dual)||chr(113)||chr(113)||chr(122)||chr(118)||chr(113))-- jgig] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 rlike (select (case when (7990=7990) then 0x31302e313031362f6a2e6d72692e323031322e30352e303031 else 0x28 end))-- qnhl] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 8732=8732-- zjdx] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 1238=(select (case when (1238=1238) then 1238 else (select 5952 union select 5256) end))-- nacc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 or extractvalue(4152,concat(0x5c,0x71766b6a71,(select (elt(4152=4152,1))),0x71717a7671))-- geqz] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012. [DOI: 10.1016/j.mri.2012.05.001 and 3200=(select upper(xmltype(chr(60)||chr(58)||chr(113)||chr(118)||chr(107)||chr(106)||chr(113)||(select (case when (3200=3200) then 1 else 0 end) from dual)||chr(113)||chr(113)||chr(122)||chr(118)||chr(113)||chr(62))) from dual)] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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