Qiao, Mengyun;
Wang, Shuo;
Qiu, Huaqi;
de Marvao, Antonio;
O’Regan, Declan P;
Rueckert, Daniel;
Bai, Wenjia;
(2024)
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.
IEEE Transactions on Medical Imaging
, 43
(3)
pp. 1259-1269.
10.1109/tmi.2023.3331982.
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Abstract
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data. The code and the trained generative model are available at https://github.com/MengyunQ/CHeart.
| Type: | Article |
|---|---|
| Title: | CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tmi.2023.3331982 |
| Publisher version: | https://doi.org/10.1109/tmi.2023.3331982 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Conditional generative model, synthetic data generation, cardiac image analysis, cardiac anatomy and motion |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217459 |
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