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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2208.13146 (eess)
[Submitted on 28 Aug 2022 (v1), last revised 10 Oct 2022 (this version, v2)]

Title:Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

Authors:Mengyun Qiao, Berke Doga Basaran, Huaqi Qiu, Shuo Wang, Yi Guo, Yuanyuan Wang, Paul M. Matthews, Daniel Rueckert, Wenjia Bai
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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 this https URL.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2208.13146 [eess.IV]
  (or arXiv:2208.13146v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2208.13146

Submission history

From: Mengyun Qiao [view email]
[v1] Sun, 28 Aug 2022 06:14:39 UTC (1,505 KB)
[v2] Mon, 10 Oct 2022 11:00:32 UTC (1,506 KB)
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