【摘要】 视网膜血管的精准分割对眼部疾病的诊断、预防及后续检测具有重要意义，近些年U-Net网络及其各种变体在医学图像分割领域达到了先进的水平。这些网络大多数都选择使用简单的最大值池化来对图像中间特征层进行下采样，这容易丢失部分信息，该研究提出了一种简单而有效的新型下采样方法PF池化（PF-pooling）方法，可以很好地将图像相邻像素信息进行融合。该研究所提出的下采样方法是个轻量级的通用模块，可以有效地集成到各种基于卷积操作的网络架构中。在DRIVE和STARE数据集上的实验结果显示，使用PF池化的U-Net模型有STARE数据集上的F1-score指标提升了1.98%，准确率提升了0.2%，灵敏度提升了3.88%。并且通过更换不同算法模型来验证，并提出模块的泛化性，结果表明PF池化在DenseUNet和Res-UNet模型上均实现了性能的提升，具有很好的普适性。更多还原

【Abstract】 Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusionpooling(PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and ResUNet models, and has good universality.更多还原