Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques
<p dir="ltr">Coronary artery disease (CAD) is a significant global health concern, emphasizing the need for reliable and automated diagnostic solutions. This study proposes a novel <u>deep learning </u>framework aimed at improving both full artery segmentation and <u&g...
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2025
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| Summary: | <p dir="ltr">Coronary artery disease (CAD) is a significant global health concern, emphasizing the need for reliable and automated diagnostic solutions. This study proposes a novel <u>deep learning </u>framework aimed at improving both full artery segmentation and <u>stenosis</u> localization by incorporating Self-Organizing <u>Neural Networks</u> (Self-ONN). The DenseSelfU-Net model leverages DenseNet121 as an encoder and a Self-ONN enhanced decoder within a U-Net based architecture to achieve robust feature extraction and precise full artery segmentation, achieving an IoU of 82.52% and a Dice score of 90.35% on the ARCADE Challenge dataset. For stenosis localization, Self-ONN is integrated into key components of the Multi-Scale <u>Attention Network</u> (MA-Net), which includes the Multi-Scale Fusion Attention Block (MFAB) and the Position-wise Attention Block (PAB), capturing complex vascular patterns through both local and global dependencies and resulting in the DenseSelfMA-Net model. The DenseSelfMA-Net achieves Dice scores of 60.59% and 60.36% and IoU scores of 46.09% and 45.36% for the MFAB and <u>PAB configurations</u>, respectively on the ARCADE challenge dataset. These results demonstrate the effectiveness of Self-ONN in enhancing diagnostic precision and facilitating early CAD diagnosis, with promising implications for clinical practice.</p><h2>Other Information</h2><p dir="ltr">Published in: Biomedical Signal Processing and Control<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.bspc.2025.108023" target="_blank">https://dx.doi.org/10.1016/j.bspc.2025.108023</a></p> |
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