15 research outputs found
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
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