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

TL;DR

TransUNext introduces an advanced U-shaped framework combining Transformer and CNN for precise retinal vessel segmentation, effectively capturing local and global features while addressing challenges like low contrast and variable vessel morphology.

Contribution

The paper presents TransUNext, a novel hybrid architecture integrating Efficient Self-attention and GMSF modules into U-Net, improving segmentation accuracy with minimal computational overhead.

Findings

Achieved state-of-the-art AUC scores on four public datasets.

Effectively captures both local and global features in vessel images.

Outperforms existing methods in retinal vessel segmentation.

Abstract

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.