基于自监督学习的病理图像层次分割

在苏木精-伊红(HE)染色病理图像中,细胞染色分布的不均匀和各类组织形态的多样性给病理图像的自动分割带来极大挑战。为解决该问题,提出了一种基于自监督学习的病理图像三步层次分割方法,对病理图像中各类组织进行由粗略到精细的全自动逐层分割。首先,根据互信息的计算结果在RGB色彩空间中进行特征选择;其次,采用K-means聚类将图像初步分割为各类组织结构的色彩稳定区域与模糊区域;然后,以色彩稳定区域为训练集采用朴素贝叶斯分类对模糊区域进行进一步分割,得到完整的细胞核、细胞质和胞外间隙这三类组织结构;最后,对细胞核部分进行结合形状和色彩强度的混合分水岭分割得到细胞核间的精确边界,进而量化计算细胞核个数、核占比、核质比等指标。对脑膜瘤HE染色病理图像的分割实验结果表明,所提方法对于染色和细胞形态差异保持较高的鲁棒性,各类组织区域分割误差在5%以内,在细胞核分割精度的对比实验中平均正确率在96%以上,满足临床自动图像分析的要求,其量化结果可以为定量病理分析提供依据。

Hierarchical segmentation of pathological images based on self-supervised learning

The uneven distribution of cell staining and the diversity of tissue morphologies bring challenges to the automatic segmentation of Hematoxylin-Eosin (HE) stained pathological images. In order to solve the problem, a three-step hierarchical segmentation method of pathological images based on self-supervised learning was proposed to automatically segment the tissues in the pathological images layer-by-layer from coarse to fine. Firstly, feature selection was performed in the RGB color space based on the calculation result of mutual information. Secondly, the image was initially segmented into stable and fuzzy color regions of each tissue structure based on K-means clustering. Thirdly, the stable color regions were taken as training datasets for further segmentation of fuzzy color regions by naive Bayesian classification, and the three complete tissue structures including nucleus, cytoplasm and extracellular space were obtained. Finally, precise boundaries between nuclei were obtained by performing the mixed watershed classification considering both shape and color intensities to the nucleus part, so as to quantitatively calculate the indicators such as the number of nuclei, nucleus proportion, and nucleus-cytoplasm ratio. Experimental results of HE stained meningioma pathological image segmentation show that， the proposed method is highly robust to the difference of staining and cell morphologies, the error of issue area segmentation is within 5%, and the average accuracy of the proposed method in nucleus segmentation accuracy experiment is above 96%, which means that the proposed method can meet the requirements of automatic analysis of clinical images and its quantitative results can provide references for quantitative pathological analysis.