应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割

肾小球滤过膜包含内皮细胞、肾小球基底膜和足细胞3层超微结构，其形态改变是诊断肾小球疾病的重要指标之一。准确的滤过膜语义分割有助于病理医生识别和判断滤过膜细微的病理改变，为相关的病理诊断提供帮助。由于肾小球滤过膜的超微病理图像不仅结构复杂而且灰度分辨率很低，传统的分割算法均只能对其中形态最简单的基底膜部分进行分割，分割精度也难以保证。本文提出基于深度学习网络DeepLab-v3的肾小球滤过膜自动语义分割算法，应用空洞卷积扩大感受野，控制图像的特征分辨率，再通过空洞空间金字塔池化来获得多尺度的图像信息，最终将肾小球滤过膜的3个组成部分同时分割出来，并均能达到较好的分割效果。本文通过对重要参数进行实验探究，使得平均分割准确度达到0.776，是目前效果相对较好的模型。

Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network

The glomerular filtration membrane contains 3 layers of ultrastructures,namely endothelial cells,glomerular basement membrane and podocytes.The morphological change of glomerular filtration membrane is one of the important indicators for the diagnosis of glomerular diseases.The accurate semantic segmentation of filtration membrane helps pathologists identify and determine the subtle pathological changes in filtration membrane so as to provide reference in the related pathological diagnosis.Due to the complicated structure and low gray-scale resolution of the ultrastructural pathology image of glomerular filtration membrane,the traditional segmentation algorithms can only segment the basement membrane with the simplest morphology,and it is difficult to guarantee the segmentation accuracy.Herein an automatic semantic segmentation algorithm based on deep learning network DeepLab-v3 for glomerular filtration membrane is proposed.Atrous convolution is used to expand field-of-views and control the feature resolution of the image;and then multi-scale image information is obtained through atrous spatial pyramid pooling;and finally,the 3 components of glomerular filtration membrane are simultaneously segmented,and all can achieve a favorable segmentation effect.Through the experimental exploration on important parameters,the average segmentation accuracy can reach 0.776,and a relatively good model at present is obtained in the study.