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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

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arxiv 2303.06274 v2 pith:DDSQYXF5 submitted 2023-03-11 cs.CV cs.LG

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Simon Graham , Quoc Dang Vu , Mostafa Jahanifar , Martin Weigert , Uwe Schmidt , Wenhua Zhang , Jun Zhang , Sen Yang
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Jinxi Xiang Xiyue Wang Josef Lorenz Rumberger Elias Baumann Peter Hirsch Lihao Liu Chenyang Hong Angelica I. Aviles-Rivero Ayushi Jain Heeyoung Ahn Yiyu Hong Hussam Azzuni Min Xu Mohammad Yaqub Marie-Claire Blache Beno\^it Pi\'egu Bertrand Vernay Tim Scherr Moritz B\"ohland Katharina L\"offler Jiachen Li Weiqin Ying Chixin Wang Dagmar Kainmueller Carola-Bibiane Sch\"onlieb Shuolin Liu Dhairya Talsania Yughender Meda Prakash Mishra Muhammad Ridzuan Oliver Neumann Marcel P. Schilling Markus Reischl Ralf Mikut Banban Huang Hsiang-Chin Chien Ching-Ping Wang Chia-Yen Lee Hong-Kun Lin Zaiyi Liu Xipeng Pan Chu Han Jijun Cheng Muhammad Dawood Srijay Deshpande Raja Muhammad Saad Bashir Adam Shephard Pedro Costa Jo\~ao D. Nunes Aur\'elio Campilho Jaime S. Cardoso Hrishikesh P S Densen Puthussery Devika R G Jiji C V Ye Zhang Zijie Fang Zhifan Lin Yongbing Zhang Chunhui Lin Liukun Zhang Lijian Mao Min Wu Vi Thi-Tuong Vo Soo-Hyung Kim Taebum Lee Satoshi Kondo Satoshi Kasai Pranay Dumbhare Vedant Phuse Yash Dubey Ankush Jamthikar Trinh Thi Le Vuong Jin Tae Kwak Dorsa Ziaei Hyun Jung Tianyi Miao David Snead Shan E Ahmed Raza Fayyaz Minhas Nasir M. Rajpoot
This is my paper
classification cs.CV cs.LG
keywords challengenuclearsegmentationanalysiscellularconicdetectiondevelopment
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

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