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

TL;DR

The CoNIC challenge advances nuclear detection, segmentation, and classification in histology images, fostering algorithm development and revealing biological insights, notably the roles of eosinophils and neutrophils in tumor microenvironments.

Contribution

This work introduces a large-scale community challenge with a comprehensive dataset to improve nuclear segmentation and cellular analysis in histology, along with detailed post-challenge analysis.

Findings

Significant improvement in downstream dysplasia grading and survival prediction.

Eosinophils and neutrophils are key in tumor microenvironment analysis.

Release of models and results to support future biomarker discovery.

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.