Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Qiao M, Basaran BD, Qiu H, Wang S, Guo Y, Wang Y, Matthews PM, Rueckert D, Bai W (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13593 LNCS
Pages Range: 3-12
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783031234422
DOI: 10.1007/978-3-031-23443-9_1
Abstract
Cardiovascular disease, the leading cause of death globally, is an age-related disease. Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression. In this work, we propose a novel conditional generative model to describe the changes of 3D anatomy of the heart during ageing. The proposed model is flexible and allows integration of multiple clinical factors (e.g. age, gender) into the generating process. We train the model on a large-scale cross-sectional dataset of cardiac anatomies and evaluate on both cross-sectional and longitudinal datasets. The model demonstrates excellent performance in predicting the longitudinal evolution of the ageing heart and modelling its data distribution. The codes are available at https://github.com/MengyunQ/AgeHeart.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Fudan University / 复旦大学
China (CN)
United Kingdom (GB)
Fudan University / 复旦大学
China (CN)
How to cite
APA:
Qiao, M., Basaran, B.D., Qiu, H., Wang, S., Guo, Y., Wang, Y.,... Bai, W. (2022). Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data. In Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-12). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
MLA:
Qiao, Mengyun, et al. "Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data." Proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang, Springer Science and Business Media Deutschland GmbH, 2022. 3-12.
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