Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10463
Title: Prompt deep light-weight vessel segmentation network (PLVS-Net)
Authors: Arsalan, Muhammad 
Khan, Tariq M. 
Naqvi, Syed Saud 
Dr. NAWAZ Mehmood 
Razzak, Imran 
Issue Date: 2023
Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, vol. 20(2), pp. 1363-1371.
Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics 
Abstract: Achieving accurate retinal vessel segmentation is critical in the progression and diagnosis of vision-threatening diseases such as diabetic retinopathy and age-related macular degeneration. Existing vessel segmentation methods are based on encoder-decoder architectures, which frequently fail to take into account the retinal vessel structure's context in their analysis. As a result, such methods have difficulty bridging the semantic gap between encoder and decoder characteristics. This paper proposes a Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) to address these issues by using prompt blocks. Each prompt block use combination of asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions to extract useful features. This novel strategy improves the performance of the segmentation network while simultaneously decreasing the number of trainable parameters. Our method outperformed competing approaches in the literature on three benchmark datasets, including DRIVE, STARE, and CHASE.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10463
ISSN: 1545-5963
1557-9964
DOI: 10.1109/TCBB.2022.3211936
Appears in Collections:Applied Data Science - Publication

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