FANet: Feature Aggregation Network With Dual Encoders for Fundus Retinal Vessel Segmentation

Abstract

Fundus retinal vessel segmentation is important for assisting in the diagnosis and monitoring of related ophthalmic diseases. Due to the fact that fundus retinal vessels have the characteristics of both local complex topology (e.g., branching structure) and global wide-area distribution, to be able to simultaneously take into account the local detail information and global context information and fully fuse the two kinds of information, this paper proposes a feature aggregation network (FANet) with dual encoders for fundus retinal vessel segmentation. Firstly, we employ the convolutional neural network (CNN) and Transformer to construct dual path encoders for extracting local detail information and global context information, respectively. Among them, to enhance the feature expression ability of the feed-forward network (FFN) in the Transformer block, we design the feature-optimized FFN (F3N). Next, we introduce the dual path feature aggregation (DPFA) module to fully fuse the feature information extracted from the CNN and Transformer paths. Finally, we introduce the multi-scale feature aggregation (MFA) module to obtain rich multi-scale information and adapt to the scale variation of vessels. Experimental results on CHASE-DB1, DRIVE, and STARE datasets demonstrate that FANet outperforms the existing mainstream segmentation methods in the comprehensive performance comparison of multiple evaluation metrics, verifying its effectiveness.