Combating Coronary Calcium Scoring Bias for Non-gated CT by Semantic Learning on Gated CT

Jiajian Li1; Anwei Li1; Jiansheng Fang1; Yonghe Hou2; Chao Song2; Huifang Yang2; Jingwen Wang1; Hongbo Liu1; Jiang Liu3

10.1109/ICCVW60793.2023.00272

2023

IEEE/CVF International Conference on Computer Vision (ICCV)

2473-9936

979-8-3503-0745-0

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

2575-2583

2-6 Oct. 2023

Paris, France

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

Coronary calcium scoring (CCS) can be quantified on non-gated or gated computed tomography (CT) for screening cardiovascular disease (CVD). And non-gated CT is used for routine coronary artery calcium (CAC) screening due to its affordability. However, artifacts of non-gated CT imaging, pose a significant challenge for automatic scoring. To combat the scoring bias caused by artifacts, we develop a novel semantic-prompt scoring siamese (SPSS) network for automatic CCS of non-gated CT. In SPSS, we establish a sharing network with regression supervised learning and semantic supervised learning. We train the SPSS by mixing non-gated CT without CAC mask and gated CT with CAC mask. In regression supervised learning, the network is trained to predict the CCS of non-gated CT. To combat the influence of motion artifacts, we introduce semantic supervised learning. We utilize gated CT to train the network to learn more accurate CAC semantic features. By integrating regression supervised learning and semantic supervised learning, the semantic information can prompt the regression supervised learning to accurately predict the CCS of non-gated CT. By conducting extensive experiments on publicly available dataset, we prove that the SPSS can alleviate the potential scoring bias introduced by pixel-wise artifact labels. Moreover, our experimental results show that the SPSS establishes state-of-the-art performance.

Computer vision Computed tomography Calcium Conferences Supervised learning Semantics Imaging

其他

英语

EI ; CPCI-S

General Program of National Natural Science Foundation of China[82272086]

Computer Science ; Imaging Science & Photographic Technology

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Imaging Science & Photographic Technology

WOS:001156680302066

20240415432407

IEEE

1.CVTE Research, China
2.Yibicom Health Management Center, CVTE, China
3.Southern University of Science and Technology, China

Jiajian Li,Anwei Li,Jiansheng Fang,et al. Combating Coronary Calcium Scoring Bias for Non-gated CT by Semantic Learning on Gated CT[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2023:2575-2583.