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全切片图像中组织切片的异质性评估。

Heterogeneity assessment of histological tissue sections in whole slide images.

机构信息

Normandie Université; UNICAEN, CLCC F. Baclesse, PATHIMAGE BioTICLA EA 4656, Caen, France.

Normandie Université; UNICAEN, CLCC F. Baclesse, PATHIMAGE BioTICLA EA 4656, Caen, France; CIALab, Department of Biomedical Informatics, OSU, Columbus, OH, USA.

出版信息

Comput Med Imaging Graph. 2015 Jun;42:51-5. doi: 10.1016/j.compmedimag.2014.11.006. Epub 2014 Nov 20.

Abstract

Computerized image analysis (IA) can provide quantitative and repeatable object measurements by means of methods such as segmentation, indexation, classification, etc. Embedded in reliable automated systems, IA could help pathologists in their daily work and thus contribute to more accurate determination of prognostic histological factors on whole slide images. One of the key concept pathologists want to dispose of now is a numerical estimation of heterogeneity. In this study, the objective is to propose a general framework based on the diffusion maps technique for measuring tissue heterogeneity in whole slide images and to apply this methodology on breast cancer histopathology digital images.

摘要

计算机化图像分析 (IA) 可以通过分割、索引、分类等方法提供定量和可重复的目标测量。嵌入在可靠的自动化系统中,IA 可以帮助病理学家完成日常工作,从而有助于更准确地确定整个幻灯片图像中的预后组织学因素。病理学家现在想要掌握的一个关键概念是对异质性进行数值估计。在本研究中,目的是提出一种基于扩散图技术的整体幻灯片图像组织异质性测量的通用框架,并将这种方法应用于乳腺癌组织病理学数字图像。

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