FDINET: Feature-Decomposition-Interaction Networks for Retinal Vessel Segmentation

Automated segmentation of retinal vessels is challenged by the complexity of curvilinear structures. In this work, we formulate the segmentation task as the decomposition and interaction of topological and scale features of vessels. The connectivity of the curvilinear structure is preserved by the topological properties while the scale features characterize the local morphology. Therefore, we propose a decomposition-then-interaction framework for retinal vessel segmentation. A multi-branch network is designed where the centerline map and scale map are obtained from the original segmentation ground truth to fully exploit these features. The features from auxiliary branches have interacted with cross attention which finally generates the masks of retinal vessels. Experiments on DRIVE, CHASE-DB1, and STARE datasets demonstrate the promising accuracy of the proposed method.