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PSAENet: Perceptual Scale Aggregation Enhancement Network for Retinal Vessel Segmentation

Published: 28 February 2024 Publication History

Abstract

Abstract- Retinal vessel segmentation is a crucial step to help doctors diagnose various retina-related diseases. The existing networks have a limited number of information flow paths which restricts their ability to extract comprehensive information. In addition, the existing networks have fixed receptive fields and cannot accurately segment lesions of different sizes. To this end, we propose a PSAENet based on spatial pyramid pooling. Its Backbone consists of two cascaded encoder-decoder sub-networks, providing more information flow paths and reducing the number of parameters by utilizing shared parameters. Each sub-network mainly includes a feature encoder module and a feature decoder module. Additionally, in the intermediate stage between the encoder and decoder, we design a Multi-scale Context Enhancement module based on feature enhancement and multi-scale information fusion, which can simulate the receptive fields of human vision to enhance the network's feature extraction ability, while utilizing multiple sizes of pooling layers to extract features and improve network performance. Our experiments show that PSAENet outperforms cutting-edge techniques in terms of performance.

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  1. PSAENet: Perceptual Scale Aggregation Enhancement Network for Retinal Vessel Segmentation

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
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    Published: 28 February 2024

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    Author Tags

    1. Multi-scale Context Enhancement module
    2. Perceptual Scale Aggregation Enhancement, Two cascaded encoder-decoder networks
    3. Retinal vessel segmentation

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