Adversarial Stain Transfer for Histopathology Image Analysis
Adversarial Stain Transfer for Histopathology Image Analysis
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DOI：
10.1109/tmi.2017.2781228
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发表时间：
2018-03-01
影响因子：
10.6
通讯作者：
Hamarneh, Ghassan
中科院分区：
文献类型：
--
作者：
BenTaieb, Aicha;Hamarneh, Ghassan
人们普遍认为，颜色信息是组织病理学组织载玻片的自动和视觉分析的核心。在实践中，病理学家依靠颜色，这反映了特定组织成分的存在，以建立诊断。类似地，自动组织病理学图像分析算法依赖于颜色或强度测量来提取组织特征。随着越来越多的数字化组织病理学图像，颜色变化及其影响已成为一个关键问题。这些变化不仅是组织载玻片制备中涉及的各种因素的结果，而且也是数字化过程本身的结果。因此，已经提出了不同的策略来减轻自动图像分析系统中与染色相关的组织不一致性。这样的技术通常依赖于收集颜色统计来跨图像执行颜色匹配。在这项工作中，我们提出了一种不同的染色归一化方法，我们称之为染色转移。我们设计了一个有区别的图像分析模型，配备了一个染色归一化组件，跨数据集转移染色。我们的模型包括学习数据集特定染色属性和图像特定颜色变换的生成网络以及任务特定网络（例如，分类器或分段网络）。该模型使用多目标成本函数进行端到端训练。我们评估所提出的方法在自动组织病理学图像分析的背景下，三个数据集和两个不同的分析任务：组织分割和分类。与各种基线相比，所提出的方法在归一化图像的精度和质量方面实现了上级结果。
It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasing access to digitized histopathology images, color variation and its implications have become a critical issue. These variations are the result of not only a variety of factors involved in the preparation of tissue slides but also in the digitization process itself. Consequently, different strategies have been proposed to alleviate stain-related tissue inconsistencies in automatic image analysis systems. Such techniques generally rely on collecting color statistics to perform color matching across images. In this work, we propose a different approach for stain normalization that we refer to as stain transfer. We design a discriminative image analysis model equipped with a stain normalization component that transfers stains across datasets. Our model comprises a generative network that learns data set-specific staining properties and image-specific color transformations as well as a task-specific network (e.g., classifier or segmentation network). The model is trained end-to-end using a multi-objective cost function. We evaluate the proposed approach in the context of automatic histopathology image analysis on three data sets and two different analysis tasks: tissue segmentation and classification. The proposed method achieves superior results in terms of accuracy and quality of normalized images compared to various baselines.