Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images
Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images
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DOI：
10.1016/j.patrec.2017.09.015
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发表时间：
2019-03-01
影响因子：
5.1
通讯作者：
Bunyak, Filiz
中科院分区：
文献类型：
--
作者：
Al-Milaji, Zahraa;Ersoy, Ilker;Bunyak, Filiz
癌症的发生、进展和治疗反应在很大程度上受到肿瘤微环境的影响。将肿瘤分为上皮区和基质区是研究肿瘤微环境及其对疾病进展影响的第一步。本文提出了一种从 H&E 染色标本图像中识别基质组织与上皮组织的新方法。该方法将基于卷积神经网络（CNN）的监督分类与无监督图像分割相结合。该方案将深度学习（特征学习和分类）的优势与图像分割的边界定位精度相结合，以增强性能。我们在斯坦福组织微阵列数据库上的实验结果表明，CNN 分类与显式图像分割的集成可以使识别的类别边界更好地遵循实际组织边界，并提高分类准确性。 (C) 2017 年由 Elsevier B.V. 出版
Initiation, progression, and therapeutic response in cancer are largely influenced by tumor microenvironment. Segmentation of tumor into epithelial vs. stromal regions constitutes the first step for the study of tumor microenvironment and its effects on disease progression. This paper proposes a new method for stromal vs. epithelial tissue identification from images of H&E stained specimens. The proposed method integrates convolutional neural networks (CNN) based supervised classification with unsupervised image segmentation. The scheme combines the strengths of deep learning (feature learning and classification) with the boundary localization accuracy of image segmentation for enhanced performance. Our experimental results on Stanford Tissue Microarray Database show that integration of CNN classification with explicit image segmentation leads to better adherence of identified class boundaries to actual tissue boundaries and improves the classification accuracy. (C) 2017 Published by Elsevier B.V.