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PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Authors :
Yongmao Wang
Sirui Wu
Junhao Jia
Source :
Applied Sciences, Vol 14, Iss 13, p 5382 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.2bacd91727f4bf3a0222b878663aab4
Document Type :
article
Full Text :
https://doi.org/10.3390/app14135382