Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
This work addresses connectivity issues in retinal vessel segmentation, which is crucial for practical computer-aided diagnosis systems, though it appears incremental as it builds upon existing U-shape networks.
The paper tackles the problem of poor connectivity in retinal vessel segmentation by proposing a recursive semantics-guided network that integrates enriched semantics into shallow layers and refines results iteratively, achieving improved performance on multiple public datasets.
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. The carefully designed recursive semantics-guided network has been extensively evaluated on several public datasets. Experimental results have shown the efficiency of the proposed method.