CHeart: A conditional spatio-temporal generative model for cardiac anatomy
File(s)
Accepted version
Author(s)
Type
Journal Article
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. We will share the code and the trained generative model at https://github.com/MengyunQ/CHeart.
Date Issued
2024-03-01
Date Acceptance
2023-11-06
Citation
IEEE Transactions on Medical Imaging, 2024, 43 (3), pp.1259-1269
ISSN
0278-0062
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1259
End Page
1269
Journal / Book Title
IEEE Transactions on Medical Imaging
Volume
43
Issue
3
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publication Status
Published
Date Publish Online
2023-11-10