Abstract
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.
Abstract (translated)
在数字化病理学中，细胞核分割是整个玻片图像分析的重要任务之一。随着深度学习的迅猛发展，最近的深度网络已经证明了核分割任务的成功执行。然而，实现良好性能的一个主要瓶颈是注释成本。一个大的网络需要大量的分段屏蔽，而这个注释任务是给病理学家的，而不是公众的。本文提出了一种弱监督核分割方法，该方法只需要对训练点进行注释。这种方法可以扩展到大的训练集，因为标记一个核的一个点要比细分割掩模便宜得多。为此，我们引入了一种新的辅助网络，称为伪边缘网，它指导分割网络识别核边缘，即使没有边缘注释。我们使用两个公共数据集对我们的方法进行了评估，结果表明该方法始终优于其他弱监督方法。
URL
https://arxiv.org/abs/1906.02924
