TTCNet: Transformer and Tubular Convolution Feature Attention Network for OCTA vessel segmentation

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique that can reveal blood flow and the detailed structure of the retinal and choroidal vasculature. However, uneven image quality and the high complexity of blood vessel distribution bring great challenges to accurate blood vessel segmentation. In this paper, we propose an OCTA blood vessel segmentation network based on the classic encoder-decoder architecture called TTCNet, which integrates a dual-branch encoder to capture global context information effectively. In addition, Snake Detail Attention Block is designed to enhance the perception of the tubular structure of blood vessels and provide richer feature representation in the network. We also design a module named Pyramid Feature Attention Module in each level of skip connection to better extract feature information at different scales. Experiments are conducted on two public datasets, OCTA_6M and OCTA_3M. G-mean Score and Area Under the Curve of our network on these two datasets are 93.05, 93.18, and 95.21, 94.95%, respectively, achieving better results than other state-of-the-art methods.