  Nucleus segmentation is an important task in medical image analysis. However,
machine learning models cannot perform well because there are large amount of
clusters of crowded nuclei. To handle this problem, existing approaches
typically resort to sophisticated hand-crafted post-processing strategies;
therefore, they are vulnerable to the variation of post-processing
hyper-parameters. Accordingly, in this paper, we devise a Boundary-assisted
Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus
segmentation. First, we propose a novel Task-aware Feature Encoding (TAFE)
network that efficiently extracts respective high-quality features for semantic
segmentation and instance boundary detection tasks. This is achieved by
carefully considering the correlation and differences between the two tasks.
Second, coarse nucleus proposals are generated based on the predictions of the
above two tasks. Third, these proposals are fed into instance segmentation
networks for more accurate prediction. Experimental results demonstrate that
the performance of BRP-Net is robust to the variation of post-processing
hyper-parameters. Furthermore, BRP-Net achieves state-of-the-art performances
on both the Kumar and CPM17 datasets. The code of BRP-Net will be released at
https://github.com/csccsccsccsc/brpnet.
