Department of Computer Technology, Anna University - MIT Campus, Chennai, India.
Department of Computer Technology, Anna University - MIT Campus, Chennai, India.
Comput Biol Med. 2022 Oct;149:106008. doi: 10.1016/j.compbiomed.2022.106008. Epub 2022 Aug 17.
Histopathological study has been shown to improve diagnosis of various disease classifications effectively as any disease condition is correlated to characteristic set of changes in the tissue structure. This study aims at developing an automated neural network system for grading brain tumors (Glioblastoma Multiforme) from histopathological images within the Whole Slide Images (WSI) of hematoxylin and eosin (H&E) stains with significant accuracy. Hematoxylin channels are extracted from the histopathological image patches using color de-convolution. Cell nuclei are precisely segmented using three level Otsu thresholding. From each segmented image, nuclei boundaries are extracted to extract nucleus level features based on their shape and size. Geometric features including ellipse eccentricities, nucleus perimeter, area, and polygon edge counts are extracted using geometric algorithms to define the nuclei boundaries of the segmented image. These features are collected for a large number of nuclei and the nuclei are clustered using the K-Means algorithm in order to create a dictionary. One of the major contributions involves the creation of dictionary of a fixed number of representative cell nuclei to speed up patch level classification. This optimal dictionary is used for clustering extracted cell nuclei and a fixed length histogram of counts on different types of nuclei is obtained. The proposed system has been tested with a total of 239600 TCGA patches of GBM and 206000 patches of LGG collected from GDC data portal and it showed good diagnosis performance with auto-classification accuracy of 97.2% compared to other state-of-art methods. Our results on segmentation and classification are encouraging, with better attainment with regard to precision and accuracy in contrast with previous models. The auto grading proposed system will act as a potential guide for pathologists to make more accurate decisions.
组织病理学研究已经证明可以有效地提高各种疾病分类的诊断准确性，因为任何疾病状况都与组织结构的特征变化相关。本研究旨在开发一种自动化神经网络系统，用于从苏木精和伊红（H&E）染色的全切片图像（WSI）中对脑肿瘤（多形性胶质母细胞瘤）进行分级，具有显著的准确性。从组织病理学图像补丁中使用颜色去卷积提取苏木精通道。使用三级 Otsu 阈值精确分割细胞核。从每个分割图像中提取核边界，以基于其形状和大小提取核级特征。使用几何算法提取几何特征，包括椭圆偏心率、核周长、面积和多边形边缘计数，以定义分割图像的核边界。收集大量细胞核的这些特征，并使用 K-Means 算法对细胞核进行聚类，以创建字典。主要贡献之一是创建具有固定数量代表性细胞核的字典，以加快补丁级分类的速度。该最优字典用于对提取的细胞核进行聚类，并获得不同类型细胞核的固定长度计数直方图。该系统已经在总共 239600 个 GBM 的 TCGA 补丁和 206000 个从 GDC 数据门户收集的 LGG 补丁上进行了测试，与其他最先进的方法相比，它显示出了良好的诊断性能，自动分类准确率为 97.2%。我们在分割和分类方面的结果令人鼓舞，与之前的模型相比，在精度和准确性方面有了更好的提高。提出的自动分级系统将成为病理学家做出更准确决策的潜在指南。
