Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network
Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network
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使用改进的深度学习网络自动分割细胞膜以评估整个幻灯片图像中的HER2状态
DOI：
10.1016/j.compbiomed.2019.05.020
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发表时间：
2019-07-01
影响因子：
7.7
通讯作者：
Kamasak, Mustafa
中科院分区：
文献类型：
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作者：
Khameneh, Fariba Damband;Razavi, Salar;Kamasak, Mustafa
乳腺组织中细胞不受控制的生长导致乳腺癌，这是影响美国女性的第二大常见癌症。正常情况下，人表皮生长因子受体2 （HER2）蛋白负责健康乳腺细胞的分裂和生长。目前使用免疫组织化学（IHC）和原位杂交（ISH）对模棱两可的病例评估HER2状态。人工对免疫组化染色显微图像进行HER2评估是一项容易出错、繁琐、观察者间变量和耗时的常规实验室工作，因为染色的多样性、重叠区域和不均匀的显著大玻片。为了解决这些问题，数字病理学提供了可重复的，自动的，客观的分析和解释整个幻灯片图像（WSI）。在本文中，我们提出了一个机器学习（ML）框架，以有效的方式对IHC乳腺癌图像进行分割、分类和量化。该方法主要包括两个部分：分类和分割。由于HER2与上皮区域的肿瘤相关，并且大多数乳腺肿瘤起源于上皮组织，因此开发一种分割不同组织结构的方法至关重要。所提出的技术由三个步骤组成。首先，提出一种基于超像素的支持向量机（SVM）特征学习分类器，对WSI中的上皮和基质区域进行分类。第二阶段，在分类上皮区域上，采用基于卷积神经网络（CNN）的分割方法对膜区域进行分割。最后，将分割的贴图合并，并评估每张幻灯片的总分。本文介绍了127张幻灯片的实验结果，并与最先进的手工方法和基于深度学习的方法进行了比较。实验表明，该方法在免疫组化染色数据上取得了良好的效果。该自动算法在基于超像素的上皮区域分类和使用CNN的膜染色分割方面优于其他方法。
The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are responsible for the division and growth of healthy breast cells. HER2 status is currently assessed using immunohistochemistry (IHC) as well as in situ hybridization (ISH) in equivocal cases. Manual HER2 evaluation of IHC stained microscopic images involves an error-prone, tedious, inter-observer variable, and time-consuming routine lab work due to diverse staining, overlapped regions, and non-homogeneous remarkable large slides. To address these issues, digital pathology offers reproducible, automatic, and objective analysis and interpretation of whole slide image (WSI). In this paper, we present a machine learning (ML) framework to segment, classify, and quantify IHC breast cancer images in an effective way. The proposed method consists of two major classifying and segmentation parts. Since HER2 is associated with tumors of an epithelial region and most of the breast tumors originate in epithelial tissue, it is crucial to develop an approach to segment different tissue structures. The proposed technique is comprised of three steps. In the first step, a superpixel-based support vector machine (SVM) feature learning classifier is proposed to classify epithelial and stromal regions from WSI. In the second stage, on classified epithelial regions, a convolutional neural network (CNN) based segmentation method is applied to segment membrane regions. Finally, divided tiles are merged and the overall score of each slide is evaluated. Experimental results for 127 slides are presented and compared with state-of-the-art handcraft and deep learning-based approaches. The experiments demonstrate that the proposed method achieved promising performance on IHC stained data. The presented automated algorithm was shown to outperform other approaches in terms of superpixel based classifying of epithelial regions and segmentation of membrane staining using CNN.