Retinal Vessel Segmentation via Cross-attention Feature Fusion

Retinal vessel segmentation from fundus images is of significant importance for detecting and diagnosing common ocular diseases. Conventional deep learning-based methods for retinal vessel segmentation follow the U-Net framework with an encoder-decoder architecture and employ skip connections for the recovery of spatial information lost during downsampling. However, skip connections cannot consistently have positive contributions to segmentation performance, which is caused by the semantic incompatibility between encoder features and decoder features. Based on this observation, we propose CaFFNet, a Cross-attention Feature Fusion Network designed specifically for retinal vessel segmentation. Specifically, we improve skip connections by introducing a Cross-attention Feature Fusion (CaFF) module, which effectively mitigates the semantic gap between encoder and decoder feature maps by leveraging the cross-attention mechanism for feature fusion. Besides, we introduce a Dual-Branch Pooling Fusion (DBPF) module to address the loss of vessel spatial information during pooling and capture contextual details more effectively, so as to improve segmentation performance. Experimental results on three fundus image datasets demonstrate that our CaFFNet outperforms current representative methods for retinal vessel segmentation.