TY - GEN
T1 - Combating Coronary Calcium Scoring Bias for Non-gated CT by Semantic Learning on Gated CT
AU - Li, Jiajian
AU - Li, Anwei
AU - Fang, Jiansheng
AU - Hou, Yonghe
AU - Song, Chao
AU - Yang, Huifang
AU - Wang, Jingwen
AU - Liu, Hongbo
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85182941139&partnerID=8YFLogxK
U2 - 10.1109/ICCVW60793.2023.00272
DO - 10.1109/ICCVW60793.2023.00272
M3 - Conference contribution
AN - SCOPUS:85182941139
T3 - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
SP - 2575
EP - 2583
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Y2 - 2 October 2023 through 6 October 2023
ER -