Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images
Abstract Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies fr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-57198-5 |
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| author | Shih-Sheng Chang Ching-Ting Lin Wei-Chun Wang Kai-Cheng Hsu Ya-Lun Wu Chia-Hao Liu Yang C. Fann |
| author_facet | Shih-Sheng Chang Ching-Ting Lin Wei-Chun Wang Kai-Cheng Hsu Ya-Lun Wu Chia-Hao Liu Yang C. Fann |
| author_sort | Shih-Sheng Chang |
| collection | DOAJ |
| description | Abstract Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice. |
| format | Article |
| id | doaj-art-dc7222da7b2c4dfe8c3ef3a8d9183d2e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dc7222da7b2c4dfe8c3ef3a8d9183d2e2025-08-20T02:20:06ZengNature PortfolioScientific Reports2045-23222024-03-0114111110.1038/s41598-024-57198-5Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic imagesShih-Sheng Chang0Ching-Ting Lin1Wei-Chun Wang2Kai-Cheng Hsu3Ya-Lun Wu4Chia-Hao Liu5Yang C. Fann6Division of Cardiovascular Medicine, China Medical University HospitalArtificial Intelligence Center, China Medical University HospitalDepartment of Neurology, China Medical University HospitalArtificial Intelligence Center, China Medical University HospitalArtificial Intelligence Center, China Medical University HospitalDivision of Cardiovascular Medicine, China Medical University HospitalDivision of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of HealthAbstract Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.https://doi.org/10.1038/s41598-024-57198-5 |
| spellingShingle | Shih-Sheng Chang Ching-Ting Lin Wei-Chun Wang Kai-Cheng Hsu Ya-Lun Wu Chia-Hao Liu Yang C. Fann Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images Scientific Reports |
| title | Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images |
| title_full | Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images |
| title_fullStr | Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images |
| title_full_unstemmed | Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images |
| title_short | Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images |
| title_sort | optimizing ensemble u net architectures for robust coronary vessel segmentation in angiographic images |
| url | https://doi.org/10.1038/s41598-024-57198-5 |
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