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Verma R, Kumar N, Patil A, Kurian NC, Rane S, Graham S, Vu QD, Zwager M, Raza SEA, Rajpoot N, Wu X, Chen H, Huang Y, Wang L, Jung H, Brown GT, Liu Y, Liu S, Jahromi SAF, Khani AA, Montahaei E, Baghshah MS, Behroozi H, Semkin P, Rassadin A, Dutande P, Lodaya R, Baid U, Baheti B, Talbar S, Mahbod A, Ecker R, Ellinger I, Luo Z, Dong B, Xu Z, Yao Y, Lv S, Feng M, Xu K, Zunair H, Hamza AB, Smiley S, Yin TK, Fang QR, Srivastava S, Mahapatra D, Trnavska L, Zhang H, Narayanan PL, Law J, Yuan Y, Tejomay A, Mitkari A, Koka D, Ramachandra V, Kini L, Sethi A. MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. IEEE Trans Med Imaging 2021; 40:3413-3423. [PMID: 34086562 DOI: 10.1109/tmi.2021.3085712] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.
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Narayanan PL, Raza SEA, Hall AH, Marks JR, King L, West RB, Hernandez L, Guppy N, Dowsett M, Gusterson B, Maley C, Hwang ES, Yuan Y. Unmasking the immune microecology of ductal carcinoma in situ with deep learning. NPJ Breast Cancer 2021; 7:19. [PMID: 33649333 PMCID: PMC7921670 DOI: 10.1038/s41523-020-00205-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
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Affiliation(s)
- Priya Lakshmi Narayanan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
| | - Shan E Ahmed Raza
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
- Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Allison H Hall
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Lorraine King
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Robert B West
- Department of Pathology, Surgical Pathology, Stanford, CA, USA
| | - Lucia Hernandez
- Department of Anatomic Pathology, Hospital Universitario, 12 de Octubre, Madrid, Spain
| | - Naomi Guppy
- Breast Cancer Now Histopathology Core, Institute of Cancer Research, London, UK
- UCL Advanced Diagnostics, University College London, London, UK
| | - Mitch Dowsett
- The Breast Cancer Now Toby Robins Research Centre, Institute of Cancer Research, London, UK
- Academic Department of Biochemistry, Royal Marsden Hospital, London, UK
| | - Barry Gusterson
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Carlo Maley
- Biodesign Center for Personalized Diagnostics and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yinyin Yuan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
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Joseph J, Roudier MP, Narayanan PL, Augulis R, Ros VR, Pritchard A, Gerrard J, Laurinavicius A, Harrington EA, Barrett JC, Howat WJ. Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images. Sci Rep 2019; 9:12845. [PMID: 31492872 PMCID: PMC6731323 DOI: 10.1038/s41598-019-49139-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 08/16/2019] [Indexed: 12/20/2022] Open
Abstract
Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.
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Affiliation(s)
- Jesuchristopher Joseph
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom.
| | - Martine P Roudier
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - Priya Lakshmi Narayanan
- Centre for Evolution and Cancer, Division of Molecular Pathology, Institute of Cancer Research London, London, United Kingdom
| | - Renaldas Augulis
- Vilnius University, Faculty of Medicine and the National Centre of Pathology, affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Vidalba Rocher Ros
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - Alison Pritchard
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - Joe Gerrard
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - Arvydas Laurinavicius
- Vilnius University, Faculty of Medicine and the National Centre of Pathology, affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Elizabeth A Harrington
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - J Carl Barrett
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
| | - William J Howat
- Molecular Pathology Group, Translational Science, AstraZeneca, Cambridge, United Kingdom
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Narayanan PL, Warton C, Rosella Boonzaier N, Molteno CD, Joseph J, Jacobson JL, Jacobson SW, Zöllei L, Meintjes EM. Improved segmentation of cerebellar structures in children. J Neurosci Methods 2015; 262:1-13. [PMID: 26743973 DOI: 10.1016/j.jneumeth.2015.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [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: 01/13/2015] [Revised: 12/09/2015] [Accepted: 12/17/2015] [Indexed: 11/27/2022]
Abstract
BACKGROUND Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available. NEW METHOD The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9-13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to 'gold standard' manual tracings. RESULTS Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55-0.91; mean Hausdorff distance 0.9 mm; range 0.8-2.7 mm). COMPARISON WITH EXISTING METHODS In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (Neuroimage 2014;95(1):217) in adults (across all lobules mean DSC=0.73, range 0.40-0.89). CONCLUSIONS CAPCA18 and the associated multi-subject atlases of the training subjects yield improved segmentation of cerebellar structures in children.
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Affiliation(s)
- Priya Lakshmi Narayanan
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa.
| | - Christopher Warton
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Natalie Rosella Boonzaier
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Christopher D Molteno
- Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jesuchristopher Joseph
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa
| | - Joseph L Jacobson
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Sandra W Jacobson
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Lilla Zöllei
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Ernesta M Meintjes
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa
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