Graham, Simon; Vu, Quoc Dang; Jahanifar, Mostafa; Weigert, Martin; Schmidt, Uwe; Zhang, Wenhua; Zhang, Jun; Yang, Sen; Xiang, Jinxi; Wang, Xiyue; Rumberger, Josef Lorenz; Baumann, Elias; Hirsch, Peter; Liu, Lihao; Hong, Chenyang; Aviles-Rivero, Angelica I; Jain, Ayushi; Ahn, Heeyoung; Hong, Yiyu; Azzuni, Hussam; ... (2024). CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical image analysis, 92(103047), p. 103047. 10.1016/j.media.2023.103047
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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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Service Sector > Institute of Pathology |
UniBE Contributor: |
Baumann, Elias |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
1361-8423 |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
03 Jan 2024 15:18 |
Last Modified: |
12 Jan 2024 00:17 |
Publisher DOI: |
10.1016/j.media.2023.103047 |
PubMed ID: |
38157647 |
Uncontrolled Keywords: |
Computational pathology Deep learning Nuclear recognition |
BORIS DOI: |
10.48350/191079 |
URI: |
https://boris.unibe.ch/id/eprint/191079 |
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