Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer.
Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer.
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关于组织学图像的大规模计算揭示了乳腺癌的分化参数。
DOI：
10.1186/1471-2342-6-14
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发表时间：
2006-10-27
影响因子：
2.7
通讯作者：
Tozeren, Aydin
中科院分区：
文献类型：
--
作者：
Petushi, Sokol;Garcia, Fernando U;Haber, Marian M;Katsinis, Constantine;Tozeren, Aydin
肿瘤的分类是不精确的，很大程度上依赖于肿瘤组织切片图像的定性病理检查。在这项研究中，我们的目的是开发一种基于癌组织纹理特征的苏木精和伊红（H&E）染色组织切片的自动计算方法。对组织学切片图像进行图像处理，检测和识别脂肪组织、细胞外基质、形态学上不同的细胞核类型和管状结构。然后将图像分析得到的纹理参数应用于监督分类方案中，以测试集的组织学等级为指导对图像进行分类。病理学家对浸润性乳腺癌图像的组织学分级与分散染色质的细胞核的存在和范围以及结构密切相关，特别是管状横切面的存在程度。本研究发现区分肿瘤分级的两个参数是(1)染色质分散的细胞核的数量密度和(2)通过图像处理识别的管状截面的数量密度，这些管状截面被连续的细胞核串包围着。基于含有高浓度癌细胞核的整张幻灯片图像的细分分类与整张幻灯片的分级分类一致。本研究提出的自动影像分析与分类，显示了基于显微纹理的组织学影像临床相关分类的可行性。该方法为病理学家提供了一种宝贵的定量工具，用于评估乳腺肿瘤分级的诺丁汉系统的组成部分，避免了观察者内部的变异性，从而增加了决策过程的一致性。
Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process.