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A plaque recognition algorithm for coronary OCT images by Dense Atrous Convolution and attention mechanism

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Publicado en:PLoS One vol. 20, no. 6 (Jun 2025), p. e0325911
Autor principal: He, Meng
Otros Autores: Zhao, Ran, Zhang, Ying, Zhang, Bo, Zhang, Cheng, Wang, Di, Sun, Jinlu
Publicado:
Public Library of Science
Materias:
Tomography
Calcification
Accuracy
Datasets
Deep learning
Algorithms
Convolution
Medical imaging
Image processing
Plaques
Automation
Lipids
Machine learning
Coronary artery
Image segmentation
Neural networks
Classification
Optical Coherence Tomography
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision support, automated detection of plaques in coronary OCT images holds paramount importance. For addressing these challenges, we propose a novel deep learning algorithm featuring Dense Atrous Convolution (DAC) and attention mechanism to realize high-precision segmentation and classification of Coronary artery plaques. Then, a relatively well-established dataset covering 760 original images, expanded to 8,000 using data enhancement. This dataset serves as a significant resource for future research endeavors. The experimental results demonstrate that the dice coefficients of calcified, fibrous, and lipid plaques are 0.913, 0.900, and 0.879, respectively, surpassing those generated by five other conventional medical image segmentation networks. These outcomes strongly attest to the effectiveness and superiority of our proposed algorithm in the task of automatic coronary artery plaque segmentation.
ISSN:1932-6203
DOI:10.1371/journal.pone.0325911
Fuente:Health & Medical Collection