标题： TransUNext：迈向更先进的U形框架以实现眼底图像中血管的自动分割 显示英文标题

标题： TransUNext: towards a more advanced U-shaped framework for automatic vessel segmentation in the fundus image

摘要： 目的：自动且准确地分割眼底血管图像已成为糖尿病等眼科疾病计算机辅助诊断的重要前提。 由于视网膜血管分支末端与背景对比度低、血管细长以及眼底血管图像中视盘和视杯形态多变等原因，高精度视网膜血管分割任务仍然面临诸多困难。 方法：我们提出了一种更先进的混合Transformer和CNN的U形架构：TransUNext，它将Efficient自注意力机制集成到U-Net的编码器和解码器中，以最小的计算开销捕获局部特征和全局依赖关系。 同时，进一步引入了全局多尺度融合（GMSF）模块来升级跳过连接，融合高级语义信息和低级细节信息，并消除高低级语义差异。 受ConvNeXt启发，设计了TransNeXt块，优化了U-Net中每个基础块的计算复杂性，并避免了当信息在不同维度的特征空间之间转换时因压缩维度而导致的信息丢失。 结果：我们在四个公开数据集DRIVE、STARE、CHASE-DB1和HRF上评估了所提出的方法。 在实验结果中，AUC（ROC曲线下的面积）值分别为0.9867、0.9869、0.9910和0.9887，超过了其他最先进的方法。 显示英文摘要

摘要： Purpose: Automatic and accurate segmentation of fundus vessel images has become an essential prerequisite for computer-aided diagnosis of ophthalmic diseases such as diabetes mellitus. The task of high-precision retinal vessel segmentation still faces difficulties due to the low contrast between the branch ends of retinal vessels and the background, the long and thin vessel span, and the variable morphology of the optic disc and optic cup in fundus vessel images. Methods: We propose a more advanced U-shaped architecture for a hybrid Transformer and CNN: TransUNext, which integrates an Efficient Self-attention Mechanism into the encoder and decoder of U-Net to capture both local features and global dependencies with minimal computational overhead. Meanwhile, the Global Multi-Scale Fusion (GMSF) module is further introduced to upgrade skip-connections, fuse high-level semantic and low-level detailed information, and eliminate high- and low-level semantic differences. Inspired by ConvNeXt, TransNeXt Block is designed to optimize the computational complexity of each base block in U-Net and avoid the information loss caused by the compressed dimension when the information is converted between the feature spaces of different dimensions. Results: We evaluated the proposed method on four public datasets DRIVE, STARE, CHASE-DB1, and HRF. In the experimental results, the AUC (area under the ROC curve) values were 0.9867, 0.9869, 0.9910, and 0.9887, which exceeded the other state-of-the-art.