  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.
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 will be released.
