MoNuSAC is an official satellite event of ISBI 2020


Post-challenge submissions are open and are due by April 30, 2020.

Challenge leaderboard and winners are final. But, we will create a separate post-challenge leaderboard for the new submissions.


Motivation

Different types of cells play a vital role in the initiation, development, invasion, metastasis and therapeutic response of tumors of various organs. For example, (1) most carcinomas originate from epithelial cells, (2) spatial arrangement of tumor infiltrating Lymphocytes (TILs) is associated with clinical outcome in several cancers, including the ones of breast, prostate, and lung (Fridman et. al., Nature Reviews Cancer, 2012), and (3) tumor associated macrophages (TAMs) influence diverse processes such as angiogenesis, neoplastic cell mitogenesis, antigen presentation, matrix degradation, and cytotoxicity in various tumors (Ruffel and Coussens, Cancer Cell, 2015). Thus, accurate identification and segmentation of nuclei of multiple cell-types is important for AI enabled characterization of tumor and its microenvironment.

In this challenge, participants will be provided with H&E stained tissue images of four organs with annotations of multiple cell-types including epithelial cells, lymphocytes, macrophages, and neutrophils. Participants will use the annotated dataset to develop computer vision algorithms to recognize these cell-types from the tissue images of unseen patients released in the testing set of the challenge. Additionally, all cell-types will not have equal number of annotated instances in the training dataset which will encourage participants to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. 


To know more about this challenge click here.

Challenge Timeline

November 15, 2019: Challenge open for registration (Please see the registration rules)

December 20, 2019: Training data release (Images + Ground Truth)

February 01, 2020: Testing data release (Images only)

March 06, 2020: Submission of testing results along with a manuscript describing the algorithm and the testing code (Please check the submission instructions)

March 16, 2020: Preliminary leaderboard will be released online

April 3, 2020: Declaration of challenge winners at ISBI 2020 challenge workshop


Post challenge publication

Algorithms of the participating teams who make it to the challenge leaderboard will be included in a post-challenge journal publication. 


Citation: To cite MoNuSAC 2020 in your work please use the following bibtex entry.
@article{monusac2020,
author = {Verma, Ruchika; Kumar, Neeraj; Patil, Abhijeet; Kurian, Nikhil; Rane, Swapnil; and Sethi, Amit},
year = {2020},
month = {02},
pages = {},
language = {en},
title = {Multi-organ Nuclei Segmentation and Classification Challenge 2020},
publisher = {Unpublished},
doi = {10.13140/RG.2.2.12290.02244/1},
 url = {http://rgdoi.net/10.13140/RG.2.2.12290.02244/1}
}


This grand challenge had 170 registrations from across the globe and only 13 teams appeared in the final leaderboard. You may use the following interactive map to know about the teams that participated in this challenge.

https://public.flourish.studio/visualisation/1630103/

The previous work done in this direction can be found in the following papers

  • Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017). A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE transactions on medical imaging36(7), 1550-1560. [PDF]
  • Kumar, Neeraj, et al. "A multi-organ nucleus segmentation challenge." IEEE transactions on medical imaging (2019). [PDF]
  • Wang, Shidan, et al. "Computational staining of pathology images to study tumor microenvironment in lung cancer." bioRxiv (2019): 630749.[PDF]
  • Hosseini, Mahdi S., et al. "Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. [PDF]
  • Sirinukunwattana, Korsuk, et al. "Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images." IEEE Trans. Med. Imaging 35.5 (2016): 1196-1206.[PDF]
  • Graham, Simon, et al. "Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images." Medical Image Analysis 58 (2019): 101563.[PDF]
  • Gamper, Jevgenij, et al. "PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification." European Congress on Digital Pathology. Springer, Cham, 2019. [PDF]