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.

本文提出了一种较少注释的、基于点的核分割方法，引入了一种叫做 PseudoEdgeNet 的辅助网络，可以引导分割网络识别核边缘，提高整个系统的性能。实验表明，在两个公共数据集上，该方法始终优于其他弱监督方法。