基于残差与小波U-Net的视网膜图像分割

视网膜血管分割是医学图像分割中常见的一项任务, 视网膜血管图像有着分割目标小而多的特点, 过去的网络在分割中可以较好地提取粗血管, 但是很容易忽略细血管, 而这部分细血管的提取在一定程度上影响网络的性能, 甚至是诊断的结果. 因此, 为了达到在保证准确提取粗血管的前提下, 提取到更多更连续的细血管的目标, 本文使用对称编解码网络作为基础网络, 使用一种新的卷积模块DR-Conv, 旨在防止过拟合的同时提高网络的学习能力. 同时, 针对最大池化层造成的信息损失问题, 考虑使用小波变换进行图像分解并使用逆小波变换对图像进行恢复, 利用混合损失函数结合不同损失函数的特性以弥补单个损失函数优化能力不足的问题. 为了评估网络的性能, 在3个公共视网膜血管数据集上分别对网络进行了测试, 并与最新方法进行了比较, 实验结果表明本文网络拥有更优的性能.

U-Net Based on Residual and Wavelet for Retinal Image Segmentation

Retinal vessel segmentation is a common task in medical image segmentation. Retinal vessel images have the characteristic of small and multiple segmentation targets. In the past, networks could effectively extract coarse blood vessels in segmentation. However, it is easy to overlook small blood vessels, the extraction of which affects the performance of the network to some extent, and even the diagnostic results. Therefore, to extract more continuous fine blood vessels while ensuring the accurate extraction of coarse blood vessels, this study uses a symmetric encoder-decoder network as the basic network and a new convolution module, DR-Conv, to prevent overfitting while improving the learning capability of the network. In the process, regarding the information loss caused by the max-pooling layer, the study uses discrete wavelet transform for image decomposition and inverse discrete wavelet transform for image reconstruction and utilizes mixed loss functions to combine the characteristics of different loss functions to compensate for the insufficient optimization ability of a single loss function. It checks the performance of the network on three public retinal vessel datasets and compares it with the latest networks, showing better performance of the proposed network.