Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset
Abstract Coronary artery calcification score (CACS), also known as the Agatston score, is a significant prognostic tool for cardiovascular disease (CVD) that utilizes computed tomography (CT). We expand our previously proposed algorithm, Residual-block Inspired Coordinate Attention U-Net (RICAU-Net)...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05785-5 |
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| author | Doyoung Park Jedidiah Ng Yixin Zhong Chun Sheng Alvin Tan Xiaomeng Wang Gillianne Geet Yi Lai Liang Zhong Su Kai Gideon Ooi Daniel Shao Weng Tan Lohendran Baskaran |
| author_facet | Doyoung Park Jedidiah Ng Yixin Zhong Chun Sheng Alvin Tan Xiaomeng Wang Gillianne Geet Yi Lai Liang Zhong Su Kai Gideon Ooi Daniel Shao Weng Tan Lohendran Baskaran |
| author_sort | Doyoung Park |
| collection | DOAJ |
| description | Abstract Coronary artery calcification score (CACS), also known as the Agatston score, is a significant prognostic tool for cardiovascular disease (CVD) that utilizes computed tomography (CT). We expand our previously proposed algorithm, Residual-block Inspired Coordinate Attention U-Net (RICAU-Net), to evaluate its generalizability on CT data from previously unseen scanners for lesion-specific CAC segmentation. The multi-vendor datasets were 1,108 CT scans acquired by Siemens, GE, Philips, and Toshiba. We created four groups of datasets, using data from the three scanners as the training and validation sets, while the last one as the test set to evaluate the algorithm on data from previously unseen scanners. RICAU-Net was trained using the datasets for automatic lesion-specific CAC segmentation and calcium scoring. The performance of lesion-specific segmentation and calcium scoring were evaluated using per-lesion Dice scores and intraclass correlation coefficient (ICC). And Bland-Altman plot analysis was conducted to examine the agreement between the CAC score derived from the prediction results and the ground truth. The proposed algorithm exhibited a mean absolute difference of less than 5% between the per-lesion Dice scores of the validation and test sets, indicating good generalizability on test sets comprised of data from unseen scanners during the training and validation phases. ICC analysis demonstrates that the Agatston scores calculated using predictions from RICAU-Net and manual segmentation exhibited excellent reliability at the per-patient level across all groups with ICC and 95% confidence intervals: 0.95 (0.95–0.96), 0.99 (0.99–1.00), 0.99 (0.99–0.99), and 1.00 (0.99–1.00) for group 1, 2, 3, and 4 respectively. Our algorithm demonstrates generalized performance on data from previously unseen scanners, making it potentially more suitable and practical for real-world clinical settings, where it will encounter diverse scanners from various organizations. Furthermore, a feasibility study using non-contrast chest CT scans indicates that the performance of our cardiac CT-trained algorithm on chest CT images was acceptable to a certain extent. |
| format | Article |
| id | doaj-art-57fe2005677b4b18b7c72cd86e013474 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-57fe2005677b4b18b7c72cd86e0134742025-08-20T03:37:30ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-05785-5Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor datasetDoyoung Park0Jedidiah Ng1Yixin Zhong2Chun Sheng Alvin Tan3Xiaomeng Wang4Gillianne Geet Yi Lai5Liang Zhong6Su Kai Gideon Ooi7Daniel Shao Weng Tan8Lohendran Baskaran9CVS.AI, National Heart Research Institute of SingaporeDuke-NUS Medical School, National University of SingaporeYong Loo Lin School of Medicine, National University of SingaporeYong Loo Lin School of Medicine, National University of SingaporeCardiovascular and Metabolic Disorders Programme, Duke-National University of SingaporeDuke-NUS Medical School, National University of SingaporeDuke-NUS Medical School, National University of SingaporeDuke-NUS Medical School, National University of SingaporeDuke-NUS Medical School, National University of SingaporeCVS.AI, National Heart Research Institute of SingaporeAbstract Coronary artery calcification score (CACS), also known as the Agatston score, is a significant prognostic tool for cardiovascular disease (CVD) that utilizes computed tomography (CT). We expand our previously proposed algorithm, Residual-block Inspired Coordinate Attention U-Net (RICAU-Net), to evaluate its generalizability on CT data from previously unseen scanners for lesion-specific CAC segmentation. The multi-vendor datasets were 1,108 CT scans acquired by Siemens, GE, Philips, and Toshiba. We created four groups of datasets, using data from the three scanners as the training and validation sets, while the last one as the test set to evaluate the algorithm on data from previously unseen scanners. RICAU-Net was trained using the datasets for automatic lesion-specific CAC segmentation and calcium scoring. The performance of lesion-specific segmentation and calcium scoring were evaluated using per-lesion Dice scores and intraclass correlation coefficient (ICC). And Bland-Altman plot analysis was conducted to examine the agreement between the CAC score derived from the prediction results and the ground truth. The proposed algorithm exhibited a mean absolute difference of less than 5% between the per-lesion Dice scores of the validation and test sets, indicating good generalizability on test sets comprised of data from unseen scanners during the training and validation phases. ICC analysis demonstrates that the Agatston scores calculated using predictions from RICAU-Net and manual segmentation exhibited excellent reliability at the per-patient level across all groups with ICC and 95% confidence intervals: 0.95 (0.95–0.96), 0.99 (0.99–1.00), 0.99 (0.99–0.99), and 1.00 (0.99–1.00) for group 1, 2, 3, and 4 respectively. Our algorithm demonstrates generalized performance on data from previously unseen scanners, making it potentially more suitable and practical for real-world clinical settings, where it will encounter diverse scanners from various organizations. Furthermore, a feasibility study using non-contrast chest CT scans indicates that the performance of our cardiac CT-trained algorithm on chest CT images was acceptable to a certain extent.https://doi.org/10.1038/s41598-025-05785-5 |
| spellingShingle | Doyoung Park Jedidiah Ng Yixin Zhong Chun Sheng Alvin Tan Xiaomeng Wang Gillianne Geet Yi Lai Liang Zhong Su Kai Gideon Ooi Daniel Shao Weng Tan Lohendran Baskaran Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset Scientific Reports |
| title | Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset |
| title_full | Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset |
| title_fullStr | Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset |
| title_full_unstemmed | Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset |
| title_short | Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset |
| title_sort | evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi vendor dataset |
| url | https://doi.org/10.1038/s41598-025-05785-5 |
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