*Image segmentation, *Transformer models, *Image processing, *Computer-assisted image analysis (Medicine), *Machine learning, *Retinal blood vessels

Scleral vessels on the surface of the human eye can provide valuable information about potential diseases or dysfunctions of specific organs, and vessel segmentation is a key step in characterizing the scleral vessels. However, accurate segmentation of blood vessels in the scleral images is a challenging task due to the intricate texture, tenuous structure, and erratic network of the scleral vessels. In this work, we propose a CNN-Transformer hybrid network named SVSNet for automatic scleral vessel segmentation. Following the typical U-shape encoder–decoder architecture, the SVSNet integrates a Sobel edge detection module to provide edge prior and further combines the Atrous Spatial Pyramid Pooling module to enhance its ability to extract vessels of various sizes. At the end of the encoding path, a vision Transformer module is incorporated to capture the global context and improve the continuity of the vessel network. To validate the effectiveness of the proposed SVSNet, comparative experiments are conducted on two public scleral image datasets, and the results show that the SVSNet outperforms other state-of-the-art models. Further experiments on three public retinal image datasets demonstrate that the SVSNet can be easily applied to other vessel datasets with good generalization capability. [ABSTRACT FROM AUTHOR]

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