Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map
复制标题
DOI：
10.1109/tmi.2018.2865709
复制
发表时间：
2019-02-01
影响因子：
10.6
通讯作者：
Walter, Thomas
中科院分区：
文献类型：
--
作者：
Naylor, Peter;Lae, Marick;Walter, Thomas
数字病理学的出现为我们提供了一个具有挑战性的机会，可以自动分析病变组织的整个切片，以获得可用于诊断和预后任务的定量图谱。特别是，对于可解释模型的开发，细胞核的检测和分割是至关重要的。在本文中，我们描述了一种新的方法，自动分割细胞核从苏木精和伊红（H&E）染色的组织病理学数据与全卷积网络。特别是，我们解决的问题，制定的分割问题作为一个回归任务的距离图分割触摸核。与使用卷积神经网络的其他方法相比，我们证明了这种方法的上级性能。
The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new method to automatically segment nuclei from Haematoxylin and Eosin (H&E) stained histopathology data with fully convolutional networks. In particular, we address the problem of segmenting touching nuclei by formulating the segmentation problem as a regression task of the distance map. We demonstrate superior performance of this approach as compared to other approaches using Convolutional Neural Networks.