A plaque recognition algorithm for coronary OCT images by Dense Atrous Convolution and attention mechanism
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| Udgivet i: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0325911 |
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Public Library of Science
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| 024 | 7 | |a 10.1371/journal.pone.0325911 |2 doi | |
| 035 | |a 3217665564 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a He, Meng | |
| 245 | 1 | |a A plaque recognition algorithm for coronary OCT images by Dense Atrous Convolution and attention mechanism | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Tomography | ||
| 653 | |a Calcification | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Convolution | ||
| 653 | |a Medical imaging | ||
| 653 | |a Image processing | ||
| 653 | |a Plaques | ||
| 653 | |a Automation | ||
| 653 | |a Lipids | ||
| 653 | |a Machine learning | ||
| 653 | |a Coronary artery | ||
| 653 | |a Image segmentation | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Optical Coherence Tomography | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhao, Ran | |
| 700 | 1 | |a Zhang, Ying | |
| 700 | 1 | |a Zhang, Bo | |
| 700 | 1 | |a Zhang, Cheng | |
| 700 | 1 | |a Wang, Di | |
| 700 | 1 | |a Sun, Jinlu | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0325911 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217665564/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3217665564/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217665564/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |