Automated plaque detection and Agatston score estimation on non-contrast CT scans: a multicenter study

Andrew M. Nguyen; Jianfei Liu; Tejas Sudharshan Mathai; Peter C. Grayson; Ronald M. Summers

doi:10.1117/12.3008750

3 April 2024 Automated plaque detection and Agatston score estimation on non-contrast CT scans: a multicenter study

Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai, Peter C. Grayson, Ronald M. Summers

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292710 (2024) https://doi.org/10.1117/12.3008750
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD). However, manual assessment of CAC often requires radiological expertise, time, and invasive imaging techniques. The purpose of this multicenter study is to validate an automated cardiac plaque detection model using a 3D multiclass nnU-Net for gated and non-gated non-contrast chest CT volumes. CT scans were performed at three tertiary care hospitals and collected as three datasets, respectively. Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes. In this work we demonstrate how the nnU-Net semantic segmentation pipeline may be adapted to detect plaques in the coronary arteries and valves. With a linear correction, nnU-Net deep learning methods may also accurately estimate Agatston scores on chest non-contrast CT scans. Compared to manual Agatson scoring, automated Agatston scoring indicated a slope of the linear regression of 0.841 with an intercept of +16 HU (R² = 0.97). These results are an improvement over previous work assessing automated Agatston score computation in non-gated CT scans.

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Citation Download Citation

Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai, Peter C. Grayson, and Ronald M. Summers "Automated plaque detection and Agatston score estimation on non-contrast CT scans: a multicenter study", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292710 (3 April 2024); https://doi.org/10.1117/12.3008750

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