Fundus image lesion segmentation method based on deep network aggregation

The invention discloses a fundus image lesion segmentation method based on deep network aggregation, and the method comprises the following steps: 1), obtaining a plurality of fundus lesion images, carrying out the manual segmentation of the lesion contour in each fundus lesion image, obtaining a true value label, and constructing a training set and a test set; 2) adding a deep aggregation networkmodule into a backbone network of the U-Net model; 3) migrating the U-Net model obtained in the step 2) to focus segmentation of the fundus image, training the U-Net model, and taking the trained U-Net model as a fundus image focus segmentation model; and 4) segmenting the fundus image to be segmented by using the fundus image lesion segmentation model. The method can effectively solve the problem of poor fundus image lesion segmentation effect based on the deep convolutional neural network in the prior art. 本发明公开了一种基于深层网络聚合的眼底图像病灶分割方法，包括以下步骤：1)获取若干眼底病灶图像，对每个眼底病灶图像中的病灶轮廓进行人工分割，得真值标签，构建训练集及测试集；2)在U-Net模型的骨干网络中添加深层聚合网络