自动化学报2017，Vol.43Issue(11)：2003-2013,11.DOI:10.16383/j.aas.2017.c160464
基于逐像素点深度卷积网络分割模型的上皮和间质组织分割
A Deep Convolutional Network for Pixel-wise Segmentation on Epithelial and Stromal Tissues in Histologic Images
摘要
Abstract
Epithelial and stromal tissues are the most common tissue breast cancer pathology images. About 80 percent breast tumors derive from mammary epithelial cells. Therefore,in order to develop computer-aided diagnosis system and analyze the micro-environment of a tumor, it is pre-requisite to segment epithelial and stromal tissues. In this paper, we propose a pixel-wise segmentation based deep convolutional network(CN-PI)model for epithelial and stromal tissues segmentation. The model initially generates two types of training patches whose central pixels are located within annotated epithelial and stromal regions. These context patches accommodate the local spatial dependencies among central pixel and its neighborhoods in the patch. During the testing phase,a square window sliding pixel-by-pixel across the entire image is used to select the context patches. The context patches are then fed to the trained CN-PI model for predicting the class labels of their central pixels. To show the effectiveness of the proposed model, the proposed CN-PI model is compared with 6 patch-wise segmentation based CN models (CN-PA) on two datasets consisting of 106 and 51 hematoxylin and eosin(H&E)stained images of breast cancer,respectively. The proposed model is shown to have F1 classification scores of 90 % and 93 %; accuracy (ACC) of 90 % and 94 %, and Matthews correlation coefficients (MCCS) of 80 % and 88 %, respectively,show improved performances over CN-PA models.关键词
深度卷积网络/乳腺组织病理图像/上皮和间质组织分割/逐像素分割Key words
Deep convolutional neural network/breast histopathological image analysis/segmentation on epithelial and stromal tissues/pixel-wise segmentation引用本文复制引用
骆小飞,徐军,陈佳梅..基于逐像素点深度卷积网络分割模型的上皮和间质组织分割[J].自动化学报,2017,43(11):2003-2013,11.基金项目
国家自然科学基金(61771249, 61273259),江苏省"六大人才高峰"高层次人才项目资助计划 (2013-XXRJ-019),江苏省自然科学基金(BK20141482),江苏创新创业团队人才计划(JS201526) 资助 Supported by National Natural Science Foundation of China (61771249, 61273259), Six Major Talents Summit of Jiangsu Province (2013-XXRJ-019), Natural Science Foundation of Jiangsu Province (BK20141482), and Jiangsu Innovation and Entrepreneurship Group Talents Plan (JS201526) （61771249, 61273259）
