Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network

• URL: http://arxiv.org/abs/2004.12776v1
• Date: Fri, 24 Apr 2020 09:18:04 GMT
• Title: Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
• Authors: Rui Xu and Tiantian Liu and Xinchen Ye and Yen-Wei Chen
• Abstract summary: 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. The carefully designed semantics-guided network has been extensively evaluated on several public datasets.
• Score: 23.936946593048987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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