Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction and thus degrades the segmentation accuracy. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper is available at \url{https://github.com/csccsccsccsc/cpp-net

翻译：细胞核分割因核密集分布和边界模糊而颇具挑战。现有方法采用多边形表示细胞核以区分接触和重叠核，并已取得显著成果。每个多边形由一组中心到边界的距离表示，这些距离通过单个细胞核中心像素的特征预测。然而，仅依赖中心像素无法提供充分的上下文信息以实现鲁棒预测，从而降低分割精度。为解决此问题，我们提出上下文感知多边形提议网络（CPP-Net）用于细胞核分割。首先，我们在每个细胞内采样点集而非单一像素进行距离预测，该策略显著增强上下文信息并提升预测鲁棒性。其次，提出基于置信度的加权模块，可自适应融合采样点集的预测结果。第三，引入新型形状感知感知损失（SAP Loss），该损失通过约束预测多边形形状提升分割质量。SAP损失基于预训练网络实现——该网络通过将中心概率图与像素到边界距离图映射至不同细胞核表示完成预训练。大量实验验证了所提CPP-Net各组件的有效性。最终，CPP-Net在三个公开数据集（DSB2018、BBBC06、PanNuke）上均达到最优性能。本文代码开源在\url{https://github.com/csccsccsccsc/cpp-net}。