Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images. (May 2023) - Liu, Xiaoming; Zhang, Di; Yao, Junping; Tang, Jinshan
Highlights: A new segmentation framework for retinal vessels in OCTA images with SOTA performance. It consists of a transformer-based branch and a convolution-based branch, information is exchanged between the inner layers. A point repair module is designed to re-predict hard regions. Adaptive gated axial transformer module is presented to reduce the computational complexity. Abstract: Optical coherence tomography angiography (OCTA) enables detailed visualization of the vascular system. OCTA is of great significance for the diagnosis and treatment of many vision-related diseases. However, accurate retinal vessel segmentation is a great challenge due to obstacles such as low vessel edge visibility and high vessel complexity. We propose a novel OCTA retinal vessel segmentation method (ARP-Net) based on the Adaptive gated axial transformer (AGAT), R esidual and Point repair modules. To reduce the impact of high vascular complexity on segmentation, we proposed a network composed of transformer and convolution branches to fuse the global and local information. Furthermore, considering the high computation of transformer, we propose an AGAT in the transformer branch. Finally, the low visibility of regions such as vessel edge in OCTA images makes the prediction of the network in these regions difficult. Therefore, we also propose a point repair module to re-predict these regions. We have performed experiments on two public OCTA vessel segmentation datasets and achieved better results than the latest state-of-the-art methods.