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血管生成组织学的计算机辅助图像处理

Computer-aided Image Processing of Angiogenic Histological.

作者信息

Sprindzuk Matvey, Dmitruk Alexander, Kovalev Vassili, Bogush Armen, Tuzikov Alexander, Liakhovski Victor, Fridman Mikhail

机构信息

United Institute of Informatics Problems, National Academy of Sciences of Belarus, Belarus.

出版信息

J Clin Med Res. 2009 Dec;1(5):249-61. doi: 10.4021/jocmr2009.12.1274. Epub 2009 Dec 28.

Abstract

UNLABELLED

This article reviews the questions regarding the image evaluation of angiogeneic histological samples, particularly the ovarian epithelial cancer. Review is focused on the principles of image analysis in the field of histology and pathology. The definition, classification, pathogenesis and angiogenesis regulation in the ovaries are also briefly discussed. It is hoped that the complex image analysis together with the patient's clinical parameters will allow an acquiring of a clear pathogenic picture of the disease, extension of the differential diagnosis and become a useful tool for the evaluation of drug effects. The challenge of the assessment of angiogenesis activity is the heterogeneity of several objects: parameters derived from patient's anamnesis as well as of pathology samples. The other unresolved problems are the subjectivity of the region of interest selection and performance of the whole slide scanning.

KEYWORDS

Angiogenesis; Image processing; Microvessel density; Cancer; Pathology.

摘要

未标注

本文回顾了有关血管生成组织学样本图像评估的问题,特别是卵巢上皮癌。综述聚焦于组织学和病理学领域的图像分析原理。还简要讨论了卵巢中的定义、分类、发病机制和血管生成调节。希望复杂的图像分析与患者的临床参数相结合,能够清晰了解疾病的致病情况,扩展鉴别诊断,并成为评估药物效果的有用工具。评估血管生成活性的挑战在于几个对象的异质性:来自患者病史以及病理样本的参数。其他未解决的问题是感兴趣区域选择的主观性和全切片扫描的性能。

关键词

血管生成;图像处理;微血管密度;癌症;病理学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28cf/3311439/a6d3695979bf/jocmr-01-249-g001.jpg

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