  Nuclei segmentation is one of the important tasks for whole slide image
analysis in digital pathology. With the drastic advance of deep learning,
recent deep networks have demonstrated successful performance of the nuclei
segmentation task. However, a major bottleneck to achieving good performance is
the cost for annotation. A large network requires a large number of
segmentation masks, and this annotation task is given to pathologists, not the
public. In this paper, we propose a weakly supervised nuclei segmentation
method, which requires only point annotations for training. This method can
scale to large training set as marking a point of a nucleus is much cheaper
than the fine segmentation mask. To this end, we introduce a novel auxiliary
network, called PseudoEdgeNet, which guides the segmentation network to
recognize nuclei edges even without edge annotations. We evaluate our method
with two public datasets, and the results demonstrate that the method
consistently outperforms other weakly supervised methods.
