Accurate retinal vessel segmentation in OCT-A images based on MsTCG-Net

Optical coherence tomography angiography (OCT-A) is a non-invasive visualization imaging technology with high-resolution that can more clearly image tiny blood vessels. Using OCT-A imaging technology, certain ophthalmic diseases can be better diagnosed by the morphological changes of retinal blood vessels. However, the task of segmenting retinal vessels is still very challenging due to the large variation in vessel size and shape and the presence of noise. In this paper, by introducing a transformer with a self-attention mechanism, we propose a novel multi-scale transformer-based channel and global attention network (MsTCG-Net) for segmentation of blood vessels in retinal OCT-A images. In MsTCG-Net, transformer-based channel joint attention (TC) block and transformer-based global joint attention (TG) block are proposed to capture multi-semantic features from spatial and channel dimension and fuse global contextual semantic features from different layers of encoder. Experimental results show that our proposed method achieves better segmentation performance than other state-of-the-art U-Net-based methods.