Medical image analysis · 2023
CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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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, Xu Min, Yaqub Mohammad, Blache Marie-Claire, Piégu Benoît, Vernay Bertrand, Scherr Tim, Böhland Moritz, Löffler Katharina, Li Jiachen, Ying Weiqin, Wang Chixin, Snead David, Raza Shan E Ahmed, Minhas Fayyaz, Rajpoot Nasir M, CoNIC Challenge Consortium
Аннотация
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.