CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Authors:
raw_author_name: Simon Graham
raw_affiliation_string: Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom
raw_author_name: Quoc Dang Vu
raw_affiliation_string: Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom
raw_author_name: Mostafa Jahanifar
raw_affiliation_string: Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom
raw_author_name: Martin Weigert
raw_affiliation_string: Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
raw_author_name: Uwe Schmidt
raw_affiliation_string: Independent Researcher, Dresden, Germany
raw_author_name: Wenhua Zhang
raw_affiliation_string: The Department of Computer Science, The University of Hong Kong, Hong Kong
raw_author_name: Jun Zhang
raw_affiliation_string: Tencent AI Lab, Shenzhen, China
raw_author_name: Sen Yang
raw_affiliation_string: College of Biomedical Engineering, Sichuan University, Chengdu, China
raw_author_name: Jinxi Xiang
raw_affiliation_string: Department of Precision Instruments, Tsinghua University, Beijing, China
raw_author_name: Xiyue Wang
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, China
raw_author_name: Josef Lorenz Rumberger
raw_affiliation_string: Charité University Medicine, Berlin, Germany; Humboldt University of Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
raw_author_name: Peter Hirsch
raw_affiliation_string: Humboldt University of Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
raw_author_name: Lihao Liu
raw_affiliation_string: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
raw_author_name: Chenyang Hong
raw_affiliation_string: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong
raw_author_name: Angelica I. Aviles-Rivero
raw_affiliation_string: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
raw_author_name: Ayushi Jain
raw_affiliation_string: PRR.ai, TX, United States of America; Softsensor.ai, Bridgewater, NJ, United States of America
raw_author_name: Heeyoung Ahn
raw_affiliation_string: Department of R&D Center, Arontier Co. Ltd, Seoul, Republic of Korea
raw_author_name: Yiyu Hong
raw_affiliation_string: Department of R&D Center, Arontier Co. Ltd, Seoul, Republic of Korea
raw_author_name: Hussam Azzuni
raw_affiliation_string: Computer Vision Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
raw_author_name: Min Xu
raw_author_name: Mohammad Yaqub
raw_author_name: Tim Scherr
raw_author_name: Moritz Böhland
raw_author_name: Jiachen Li
raw_affiliation_string: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
raw_author_name: Weiqin Ying
raw_author_name: David Snead
raw_author_name: Shan E. Ahmed Raza
raw_author_name: Fayyaz Minhas
raw_author_name: Nasir M. Rajpoot

Abstract: 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.