MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Ruchika Verma; Neeraj Kumar; Abhijeet Patil; Nikhil Cherian Kurian; Swapnil Rane; Simon Graham; Quoc Dang Vu; Mieke Zwager; Shan E.Ahmed Raza; Nasir Rajpoot; Xiyi Wu; Huai Chen; Yijie Huang; Lisheng Wang; Hyun Jung; G. Thomas Brown; Yanling Liu; Shuolin Liu; Seyed Alireza Fatemi Jahromi; Ali Asghar Khani; Ehsan Montahaei; Mahdieh Soleymani Baghshah; Hamid Behroozi; Pavel Semkin; Alexandr Rassadin; Prasad Dutande; Romil Lodaya; Ujjwal Baid; Bhakti Baheti; Sanjay Talbar; Amirreza Mahbod; Rupert Ecker; Isabella Ellinger; Zhipeng Luo; Bin Dong; Zhengyu Xu; Yuehan Yao; Shuai Lv; Ming Feng; Kele Xu; Hasib Zunair; Abdessamad Ben Hamza; Steven Smiley; Tang Kai Yin; Qi Rui Fang; Shikhar Srivastava; Dwarikanath Mahapatra; Lubomira Trnavska; Hanyun Zhang; Priya Lakshmi Narayanan; Justin Law; Yinyin Yuan; Abhiroop Tejomay; Aditya Mitkari; Dinesh Koka; Vikas Ramachandra; Lata Kini; Amit Sethi

doi:10.1109/TMI.2021.3085712

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Ruchika Verma^*, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, Simon Graham, Quoc Dang Vu, Mieke Zwager, Shan E.Ahmed Raza, Nasir Rajpoot, Xiyi Wu, Huai Chen, Yijie Huang, Lisheng Wang, Hyun Jung, G. Thomas Brown, Yanling Liu, Shuolin Liu, Seyed Alireza Fatemi Jahromi, Ali Asghar KhaniEhsan Montahaei, Mahdieh Soleymani Baghshah, Hamid Behroozi, Pavel Semkin, Alexandr Rassadin, Prasad Dutande, Romil Lodaya, Ujjwal Baid, Bhakti Baheti, Sanjay Talbar, Amirreza Mahbod, Rupert Ecker, Isabella Ellinger, Zhipeng Luo, Bin Dong, Zhengyu Xu, Yuehan Yao, Shuai Lv, Ming Feng, Kele Xu, Hasib Zunair, Abdessamad Ben Hamza, Steven Smiley, Tang Kai Yin, Qi Rui Fang, Shikhar Srivastava, Dwarikanath Mahapatra, Lubomira Trnavska, Hanyun Zhang, Priya Lakshmi Narayanan, Justin Law, Yinyin Yuan, Abhiroop Tejomay, Aditya Mitkari, Dinesh Koka, Vikas Ramachandra, Lata Kini, Amit Sethi

^*Corresponding author voor dit werk

Onderzoeksoutput: Article › Academic › peer review

131 Citaten (Scopus)

1833 Downloads (Pure)

Samenvatting

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.

Originele taal-2	English
Pagina's (van-tot)	3413-3423
Aantal pagina's	11
Tijdschrift	Ieee transactions on medical imaging
Volume	40
Nummer van het tijdschrift	12
Vroegere onlinedatum	4-jun.-2021
DOI's	https://doi.org/10.1109/TMI.2021.3085712
Status	Published - 1-dec.-2021

Toegang tot document

10.1109/TMI.2021.3085712

MoNuSAC2020_A_Multi-Organ_Nuclei_Segmentation_and_Classification_ChallengeFinal publisher's version, 1,4 MBLicentie: Taverne

Handle.net

Permanente koppeling

Citeer dit

Verma, R., Kumar, N., Patil, A., Kurian, N. C., Rane, S., Graham, S., Vu, Q. D., Zwager, M., Raza, S. E. A., Rajpoot, N., Wu, X., Chen, H., Huang, Y., Wang, L., Jung, H., Thomas Brown, G., Liu, Y., Liu, S., Jahromi, S. A. F., ... Sethi, A. (2021). MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. Ieee transactions on medical imaging, 40(12), 3413-3423. https://doi.org/10.1109/TMI.2021.3085712

@article{4639c9a9891843fd85fe8ba1648b51e6,

title = "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge",

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.",

keywords = "computational pathology, instance segmentation, Multi-organ dataset, nucleus classification, panoptic quality",

author = "Ruchika Verma and Neeraj Kumar and Abhijeet Patil and Kurian, {Nikhil Cherian} and Swapnil Rane and Simon Graham and Vu, {Quoc Dang} and Mieke Zwager and Raza, {Shan E.Ahmed} and Nasir Rajpoot and Xiyi Wu and Huai Chen and Yijie Huang and Lisheng Wang and Hyun Jung and {Thomas Brown}, G. and Yanling Liu and Shuolin Liu and Jahromi, {Seyed Alireza Fatemi} and Khani, {Ali Asghar} and Ehsan Montahaei and Baghshah, {Mahdieh Soleymani} and Hamid Behroozi and Pavel Semkin and Alexandr Rassadin and Prasad Dutande and Romil Lodaya and Ujjwal Baid and Bhakti Baheti and Sanjay Talbar and Amirreza Mahbod and Rupert Ecker and Isabella Ellinger and Zhipeng Luo and Bin Dong and Zhengyu Xu and Yuehan Yao and Shuai Lv and Ming Feng and Kele Xu and Hasib Zunair and Hamza, {Abdessamad Ben} and Steven Smiley and Yin, {Tang Kai} and Fang, {Qi Rui} and Shikhar Srivastava and Dwarikanath Mahapatra and Lubomira Trnavska and Hanyun Zhang and Narayanan, {Priya Lakshmi} and Justin Law and Yinyin Yuan and Abhiroop Tejomay and Aditya Mitkari and Dinesh Koka and Vikas Ramachandra and Lata Kini and Amit Sethi",

note = "Funding Information: Manuscript received April 18, 2021; accepted May 23, 2021. Date of publication June 4, 2021; date of current version November 30, 2021. The work of Simon Graham, Shan E. Ahmed Raza, and Nasir Rajpoot was supported by the Data to Early Diagnosis and Precision Medicine Strand of the Government{\textquoteright}s Industrial Strategy Challenge Fund, managed and delivered by U.K. Research and Innovation (UKRI) and the authors are a part of the PathLAKE Digital Pathology Consortium. The work of Amirreza Mahbod was supported by the Austrian Research Promotion Agency (FFG) under Grant 872636. (Corresponding author: Ruchika Verma.) Please see the Acknowledgment section of this article for the author affiliations. Publisher Copyright: {\textcopyright} 1982-2012 IEEE.",

year = "2021",

month = dec,

day = "1",

doi = "10.1109/TMI.2021.3085712",

language = "English",

volume = "40",

pages = "3413--3423",

journal = "Ieee transactions on medical imaging",

issn = "0278-0062",

publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

number = "12",

}

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, Thomas Brown, G, 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 2021, 'MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge', Ieee transactions on medical imaging, vol. 40, nr. 12, blz. 3413-3423. https://doi.org/10.1109/TMI.2021.3085712

TY - JOUR

T1 - MoNuSAC2020

T2 - A Multi-Organ Nuclei Segmentation and Classification Challenge

AU - Verma, Ruchika

AU - Kumar, Neeraj

AU - Patil, Abhijeet

AU - Kurian, Nikhil Cherian

AU - Rane, Swapnil

AU - Graham, Simon

AU - Vu, Quoc Dang

AU - Zwager, Mieke

AU - Raza, Shan E.Ahmed

AU - Rajpoot, Nasir

AU - Wu, Xiyi

AU - Chen, Huai

AU - Huang, Yijie

AU - Wang, Lisheng

AU - Jung, Hyun

AU - Thomas Brown, G.

AU - Liu, Yanling

AU - Liu, Shuolin

AU - Jahromi, Seyed Alireza Fatemi

AU - Khani, Ali Asghar

AU - Montahaei, Ehsan

AU - Baghshah, Mahdieh Soleymani

AU - Behroozi, Hamid

AU - Semkin, Pavel

AU - Rassadin, Alexandr

AU - Dutande, Prasad

AU - Lodaya, Romil

AU - Baid, Ujjwal

AU - Baheti, Bhakti

AU - Talbar, Sanjay

AU - Mahbod, Amirreza

AU - Ecker, Rupert

AU - Ellinger, Isabella

AU - Luo, Zhipeng

AU - Dong, Bin

AU - Xu, Zhengyu

AU - Yao, Yuehan

AU - Lv, Shuai

AU - Feng, Ming

AU - Xu, Kele

AU - Zunair, Hasib

AU - Hamza, Abdessamad Ben

AU - Smiley, Steven

AU - Yin, Tang Kai

AU - Fang, Qi Rui

AU - Srivastava, Shikhar

AU - Mahapatra, Dwarikanath

AU - Trnavska, Lubomira

AU - Zhang, Hanyun

AU - Narayanan, Priya Lakshmi

AU - Law, Justin

AU - Yuan, Yinyin

AU - Tejomay, Abhiroop

AU - Mitkari, Aditya

AU - Koka, Dinesh

AU - Ramachandra, Vikas

AU - Kini, Lata

AU - Sethi, Amit

N1 - Funding Information: Manuscript received April 18, 2021; accepted May 23, 2021. Date of publication June 4, 2021; date of current version November 30, 2021. The work of Simon Graham, Shan E. Ahmed Raza, and Nasir Rajpoot was supported by the Data to Early Diagnosis and Precision Medicine Strand of the Government’s Industrial Strategy Challenge Fund, managed and delivered by U.K. Research and Innovation (UKRI) and the authors are a part of the PathLAKE Digital Pathology Consortium. The work of Amirreza Mahbod was supported by the Austrian Research Promotion Agency (FFG) under Grant 872636. (Corresponding author: Ruchika Verma.) Please see the Acknowledgment section of this article for the author affiliations. Publisher Copyright: © 1982-2012 IEEE.

PY - 2021/12/1

Y1 - 2021/12/1

N2 - 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.

AB - 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.

KW - computational pathology

KW - instance segmentation

KW - Multi-organ dataset

KW - nucleus classification

KW - panoptic quality

U2 - 10.1109/TMI.2021.3085712

DO - 10.1109/TMI.2021.3085712

M3 - Article

C2 - 34086562

AN - SCOPUS:85107351463

SN - 0278-0062

VL - 40

SP - 3413

EP - 3423

JO - Ieee transactions on medical imaging

JF - Ieee transactions on medical imaging

IS - 12

ER -

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Samenvatting

Toegang tot document

Handle.net

Vingerafdruk

Citeer dit