文档介绍：Histopathological Image Analysis Using Model-Based
Intermediate Representations and Color Texture:
Follicular Lymphoma Grading
滤泡淋巴瘤分级
——使用基于中间表示法模型的和颜色纹理信息分析组织病理图像
Olcay Sertel · Jun Kong · Umit V. Catalyurek ·
Gerard Lozanski · Joel H. Saltz · Metin N. Gurcan
Abstract
Follicular lymphoma (FL) is a cancer of lymph system and it is the second most common lymphoid malignancy in the western world. Currently, the risk stratification of FL relies on histological grading method, where pathologists evaluate hematoxilin and eosin (H&E) stained tissue sections under a microscope as recommended by the World Health Organization.
This manual method requires intensive labor in nature. Due to the sampling bias, it also suffers from inter- and intra-reader variability and poor reproducibility. We are developing a computer-assisted system to provide quantitative assessment of FL images for more consistent evaluation of FL. In this study, we proposed a statistical framework to classify FL images based on their histological grades. We introduced model-based intermediate representation (MBIR) of cytological components that enables higher level semantic description of tissue characteristics. Moreover, we introduced a novel color-texture analysis approach that combines the MBIR with low level texture features, which capture tissue characteristics at pixel level. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the accuracy of the system significantly. The implemented system can identify the most aggressive FL (grade III) with % sensitivity and % specificity and the overall classification accuracy of the system is %.
摘要
滤泡淋巴瘤(FL)是一种淋巴系统癌症,它是西方世界排名第二的恶性淋巴肿瘤。目前，FL的恶性分级依赖于组织病理图像。目前，FL的恶性分级依赖于组织学分级方法，由世界卫生组织建议病理学家在显微镜下评估由苏木精-伊红染色法（H＆E）染色的组织切片。这种人工方法需要很大的精力。由于抽样误差，它也受到来自医生间和医生本身的差异和不可重复性的约束。我们正在开发的电脑辅助系统对FL图像进行定量评估以便提供更一致的FL评价。在这项研究中，我们提出了一个统计框架，根据其组织学分级分类FL图像。我们推出了基于模型的中间表示（
MBIR）的细胞学部件，实现了更高层次的语义描述的组