Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
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DOI：
10.1101/2020.08.01.231639
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发表时间：
2020-08
影响因子：
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通讯作者：
Joshua J. Levy;C. Haudenschild;C. Barwick;B. Christensen;L. Vaickus
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文献类型：
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作者：
Joshua J. Levy;C. Haudenschild;C. Barwick;B. Christensen;L. Vaickus
全切片图像（WSI）是来自各种患者来源（活检、切除、剥脱、液体）的染色组织薄切片的数字化表示，在任何给定的空间维度上通常超过100，000像素。数字病理学的深度学习方法通常从子图像（补丁）中提取信息，并将子图像视为独立实体，忽略重要的大规模架构关系的贡献信息。可以捕获组织块邻域之间的高阶依赖关系的建模方法已经证明了提高预测准确性的潜力，同时捕获用于预后、诊断和与其他组学模式整合的最重要的载玻片水平信息。在这里，我们回顾了两种有前途的方法，用于捕获组织学图像的宏观和微观架构，图形神经网络，它通过消息传递将来自邻居的补丁级信息上下文化，拓扑数据分析，它将上下文信息提炼成其基本组成部分。我们介绍了一个建模框架，WSI-GTFE，它集成了这两种方法，以确定和量化关键的致病信息途径。为了证明一个简单的用例，我们利用这些拓扑方法来开发肿瘤侵袭评分以分期结肠癌。
Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.