基于深度学习的视网膜眼底图像的渗出分割方法研究

Research on exudate segmentation method for retinal fundus images based on deep learning

目的探索基于深度学习方法自动分割彩色眼底图像上糖尿病患者视网膜渗出特征的可行性。方法应用研究。基于印度糖尿病视网膜病变图像数据集(IDRID)模型的U型网络,将深度残差卷积引入到编码和解码阶段,使其能够有效提取渗出深度特征,解决过拟合和特征干扰问题,同时提升模型的特征表达能力和轻量化性能。此外,通过引入改进的上下文提取模块,使模型能捕捉更广泛的特征信息,增强对视网膜病灶的感知能力,尤其是提升捕捉微小细节和模糊边缘的能力。最后,引入卷积三重注意力机制,使模型能自动学习特征权重,关注重要特征,并从多个尺度提取有益信息。通过查准率、查全率、Dice系数、准确率和灵敏度来评估模型对彩色眼底图像上糖尿病患者自动视网膜渗出特征的检测与分割能力。结果应用本文研究方法后,改进模型在IDRID数据集上的查准率、查全率、相似系数、准确率、灵敏度、分别达到81.56%、99.54%、69.32%、65.36%、78.33%。与原始模型相比,改进模型的查准率和Dice系数分别提升了2.35%和3.35%。结论基于U型网络的分割方法能自动检测并分割出糖尿病患者眼底图像的视网膜渗出特征,对于辅助医生更准确地诊断疾病情况具有重要意义。

Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images.Methods An applied study.The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset(IDRID)dataset,introduces deep residual convolution into the encoding and decoding stages,which can effectively extract seepage depth features,solve overfitting and feature interference problems,and improve the model's feature expression ability and lightweight performance.In addition,by introducing an improved context extraction module,the model can capture a wider range of feature information,enhance the perception ability of retinal lesions,and perform excellently in capturing small details and blurred edges.Finally,the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights,focus on important features,and extract useful information from multiple scales.Accuracy,recall,Dice coefficient,accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images.Results After applying this method,the accuracy,recall,dice coefficient,accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%,99.54%,69.32%,65.36%and 78.33%,respectively.Compared with the original model,the accuracy and Dice index of the improved model are increased by 2.35%,3.35%respectively.Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients,which is of great significance for assisting doctors to diagnose diseases more accurately.