Abstract:
Retinal vessel segmentation can improve the judgment ability of intelligent disease diagnosis system. Although a large number of retinal vessel segmentation models have b...Show MoreMetadata
Abstract:
Retinal vessel segmentation can improve the judgment ability of intelligent disease diagnosis system. Although a large number of retinal vessel segmentation models have been proposed with the development of deep learning, it is still a challenging task. In this work, we propose a new retinal vessel segmentation network via spatial-temporal and self-attention encoding modules, called STSANet, which can significantly improve the performance and robustness of segmentation. The spatial-temporal information of fundus images are extracted by a Spatial-Temporal encoding module in the STSANet. In addition, the internal correlation of features is captured by the Self-Attention module. By fusing spatial-temporal and self-attention features, the final result contains both spatial-temporal information and internal feature information of fundus images. The experimental results indicate that our STSANet outperforms other state-of-the-art retinal segmentation models on the published standard datasets.
Published in: 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)
Date of Conference: 01-03 November 2022
Date Added to IEEE Xplore: 15 February 2023
ISBN Information: