학술논문
MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge
Document Type
Periodical
Author
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.; Brown, G.T.; Liu, Y.; Liu, S.; Jahromi, S.A.F.; Khani, A.A.; Montahaei, E.; Baghshah, M.S.; 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, A.B.; Smiley, S.; Yin, T.; Fang, Q.; Srivastava, S.; Mahapatra, D.; Trnavska, L.; Zhang, H.; Narayanan, P.L.; Law, J.; Yuan, Y.; Tejomay, A.; Mitkari, A.; Koka, D.; Ramachandra, V.; Kini, L.; Sethi, A.
Source
IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging Medical Imaging, IEEE Transactions on. 40(12):3413-3423 Dec, 2021
Subject
Language
ISSN
0278-0062
1558-254X
1558-254X
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.