CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation.

Abstract

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. To differentiate between touching and overlapping nuclei, recent approaches have represented nuclei in the form of polygons, 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, the use of the centroid pixel alone does not provide sufficient contextual information for robust prediction and therefore affects the segmentation accuracy. To address this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than a single pixel within each cell for distance prediction; this strategy substantially enhances the contextual information and thereby improves the prediction robustness. Second, we propose a Confidence-basedWeighting 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. This 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 demonstrate 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. The code of this paper will be released.