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.

翻译：细胞核检测、分割及形态计量分析对于我们深入理解组织学与患者预后之间的关系至关重要。为推动该领域的创新，我们利用当前规模最大的同类数据集组织了一次社区范围内的挑战赛，旨在评估细胞核分割与细胞组成分析能力。该挑战赛命名为CoNIC，通过公开排行榜上的实时结果检查功能，促进了可复现性细胞识别算法的开发。基于顶级性能模型，我们使用1,658张结肠组织全切片图像开展了广泛的赛后分析。每款模型检测约7亿个细胞核，其相关特征被用于异型增生分级和生存分析，结果表明该挑战赛相较于先前技术水平的提升显著增强了下游任务性能。此外，我们的发现还提示嗜酸性粒细胞和中性粒细胞在肿瘤微环境中具有重要作用。我们公开挑战赛模型及全切片图像级别结果，以推动生物标志物发现领域更多方法的研发。