Abstract
Coronary artery disease (CAD) poses a significant challenge to cardiovascular patients worldwide, underscoring the crucial role of automated CAD diagnostic technology in clinical settings. Previous methods for diagnosing CAD using coronary artery CT angiography (CCTA) images have certain limitations in widespread replication and clinical application due to the high demand for annotated medical imaging data. In this work, we introduce the Spatio-temporal Contrast Network (SC-Net) for the first time, designed to tackle the challenges of data-efficient learning in CAD diagnosis based on CCTA. SC-Net utilizes data augmentation to facilitate clinical feature learning and leverages spatio-temporal prediction-contrast based on dual tasks to maximize the effectiveness of limited data, thus providing clinically reliable predictive results. Experimental findings from a dataset comprising 218 CCTA images from diverse patients demonstrate that SC-Net achieves outstanding performance in automated CAD diagnosis with a reduced number of training samples. The introduction of SC-Net presents a practical data-efficient learning strategy, thereby facilitating the implementation and application of automated CAD diagnosis across a broader spectrum of clinical scenarios. The source code is publicly available at the following link (https://github.com/PerceptionComputingLab/SC-Net).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelrahman, K.M., Chen, M.Y., Dey, A.K., Virmani, R., Finn, A.V., Khamis, R.Y., Choi, A.D., Min, J.K., Williams, M.C., Buckler, A.J., et al.: Coronary computed tomography angiography from clinical uses to emerging technologies: Jacc state-of-the-art review. Journal of the American College of Cardiology 76(10), 1226–1243 (2020)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. pp. 213–229. Springer (2020)
Denzinger, F., Wels, M., Ravikumar, N., Breininger, K., Reidelshöfer, A., Eckert, J., Sühling, M., Schmermund, A., Maier, A.: Coronary artery plaque characterization from ccta scans using deep learning and radiomics. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV 22. pp. 593–601. Springer (2019)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 1440–1448 (2015)
Kagiyama, N., Shrestha, S., Farjo, P.D., Sengupta, P.P.: Artificial intelligence: practical primer for clinical research in cardiovascular disease. Journal of the American Heart Association 8(17), e012788 (2019)
Leipsic, J., Abbara, S., Achenbach, S., Cury, R., Earls, J.P., Mancini, G.J., Nieman, K., Pontone, G., Raff, G.L.: Scct guidelines for the interpretation and reporting of coronary ct angiography: a report of the society of cardiovascular computed tomography guidelines committee. Journal of cardiovascular computed tomography 8(5), 342–358 (2014)
Luo, G., Ma, X., Guo, J., Zou, M., Wang, W., Cao, Y., Wang, K., Li, S.: Trajectory-aware adaptive imaging clue analysis for guidewire artifact removal in intravascular optical coherence tomography. IEEE Journal of Biomedical and Health Informatics (2023)
Ma, X., Luo, G., Wang, W., Wang, K.: Transformer network for significant stenosis detection in ccta of coronary arteries. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24. pp. 516–525. Springer (2021)
North, B.J., Sinclair, D.A.: The intersection between aging and cardiovascular disease. Circulation research 110(8), 1097–1108 (2012)
Tejero-de Pablos, A., Huang, K., Yamane, H., Kurose, Y., Mukuta, Y., Iho, J., Tokunaga, Y., Horie, M., Nishizawa, K., Hayashi, Y., et al.: Texture-based classification of significant stenosis in ccta multi-view images of coronary arteries. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22. pp. 732–740. Springer (2019)
Pagliaro, B.R., Cannata, F., Stefanini, G.G., Bolognese, L.: Myocardial ischemia and coronary disease in heart failure. Heart Failure Reviews 25(1), 53–65 (2020)
Rajon, D., Bolch, W.E.: Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. Computerized Medical Imaging and Graphics 27(5), 411–435 (2003)
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 658–666 (2019)
Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., Gulyás, B.: 3d deep learning on medical images: a review. Sensors 20(18), 5097 (2020)
Stewart, R., Andriluka, M., Ng, A.Y.: End-to-end people detection in crowded scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2325–2333 (2016)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
Xu, Y., Liang, G., Hu, G., Yang, Y., Geng, J., Saha, P.K.: Quantification of coronary arterial stenoses in cta using fuzzy distance transform. Computerized Medical Imaging and Graphics 36(1), 11–24 (2012)
Zhang, Y., Ma, J., Li, J.: Coronary r-cnn: Vessel-wise method for coronary artery lesion detection and analysis in coronary ct angiography. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part III. pp. 207–216. Springer (2022)
Zreik, M., Van Hamersvelt, R.W., Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: A recurrent cnn for automatic detection and classification of coronary artery plaque and stenosis in coronary ct angiography. IEEE transactions on medical imaging 38(7), 1588–1598 (2018)
Zuluaga, M.A., Magnin, I.E., Hernández Hoyos, M., Delgado Leyton, E.J., Lozano, F., Orkisz, M.: Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines. International journal of computer assisted radiology and surgery 6, 163–174 (2011)
Acknowledgments
This work was supported by the Key Research & Development Program of Heilongjiang Province under Grant 2023X01A08, the National Natural Science Foundation of China under Grants 62272135, 62372135, and the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Awards No. FCC/1/1976-44-01, FCC/1/1976-45-01, and REI/1/5234-01-01.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, X. et al. (2024). Spatio-Temporal Contrast Network for Data-Efficient Learning of Coronary Artery Disease in Coronary CT Angiography. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15011. Springer, Cham. https://doi.org/10.1007/978-3-031-72120-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-72120-5_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72119-9
Online ISBN: 978-3-031-72120-5
eBook Packages: Computer ScienceComputer Science (R0)
