Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study
Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study
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DOI：
10.1007/s10278-016-9873-1
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发表时间：
2016-08-01
影响因子：
4.4
通讯作者：
Elmore, Joann G.
中科院分区：
文献类型：
--
作者：
Mercan, Ezgi;Aksoy, Selim;Elmore, Joann G.
全幻灯片数字成像技术使研究人员能够研究病理学家在看数字幻灯片时的解释行为，并对诊断性医疗决策过程有了新的认识。在这项研究中，我们提出了一个简单而重要的分析，从跟踪记录中提取诊断相关的兴趣区域（roi），仅使用病理学家在整个幻灯片数字成像格式（缩放，平移和固定）中查看活检标本时的动作。我们在基于颜色和纹理特征的视觉词袋模型中使用这些提取的区域来预测整个幻灯片图像的诊断相关roi。在交叉验证设置中使用逻辑回归分类器，对240个数字乳腺活检切片和三位专家病理学家的视口跟踪日志进行交叉验证，我们生成的概率图显示，与病理学家观察的实际区域有74%的重叠。我们通过改变字典大小、视觉词定义（补丁vs超像素）和训练数据（自动提取roi vs手动标记roi）来比较不同的词袋模型。这项研究是了解病理学家的扫描行为和诊断错误的潜在原因的第一步。
Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.