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Journal Articles Medical Image Analysis Year : 2024

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

Simon Graham
Quoc Dang Vu
  • Function : relator_co_first_author
Jun Zhang
Xiyue Wang
Lihao Liu
Ayushi Jain
  • Function : Author
Heeyoung Ahn
  • Function : Author
Yiyu Hong
  • Function : Author
Tim Scherr
Moritz Böhland
Katharina Löffler
Shan Raza

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.

Dates and versions

hal-04408256 , version 1 (21-01-2024)

Licence

Attribution - NonCommercial - NoDerivatives

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Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, et al.. CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical Image Analysis, 2024, 92, pp.103047. ⟨10.1016/j.media.2023.103047⟩. ⟨hal-04408256⟩
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