Sonal Kothari, John H Phan, Adeboye O Osunkoya, May D Wang
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引用次数: 0
摘要
我们提出了一个研究组织病理学全切片图像(WSI)视觉形态模式的框架。图像表示是组织病理学癌症诊断计算机辅助决策支持系统的重要组成部分。此类系统从数字化组织活检切片中提取数百个定量图像特征,并生成预测模型。这些模型的性能取决于信息特征的识别,以便从异构的 WSI 中选择适当的感兴趣区(ROI)并开发模型。然而,由于人类对视觉形态模式的解释与定量图像特征之间存在语义差距,因此信息特征的识别受到阻碍。为了应对这一挑战,我们利用数据挖掘和信息可视化工具来研究从 WSI 的子截面中提取的特征所形成的空间模式。利用癌症基因组图谱(TCGA)提供的卵巢浆液性囊腺癌(OvCa)WSIs,我们证明了(1)单个和(2)多元图像特征对应于生物相关的 ROI,以及(3)监督图像特征选择可以将组织病理学领域的知识映射到定量图像特征。
Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images.
We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.