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：核探测、分解和光谱特征分析对于帮助我们进一步理解病理学和病人结果之间的关系至关重要。为了推动这一领域的创新,我们利用最大的现有数据组来评估核分解和细胞构成,建立了一个全社区的挑战。我们的挑战名为CONIC,它刺激开发了细胞识别的可复制算法,对公共领头板进行实时结果检查。我们利用1 658个结肠组织全流图像的顶级模型进行了广泛的挑战后分析。每模型中检测到约7亿核,相关特征被用来进行痢疾定级和生存分析,我们在那里展示了挑战相对于以往的状态的改善导致下游性能的显著提高。我们的调查结果还表明,电子生物学家和中脊椎动物在肿瘤微积分中起着重要作用。我们发布了挑战模型和WSI级结果,以促进进一步开发生物标记发现方法。</s>