TY - JOUR
T1 - Cardiac aging synthesis from cross-sectional data with conditional generative adversarial networks
AU - Campello, Víctor M.
AU - Xia, Tian
AU - Liu, Xiao
AU - Sanchez, Pedro
AU - Martín-isla, Carlos
AU - Petersen, Steffen E
AU - Seguí, Santi
AU - Tsaftaris, Sotirios A.
AU - Lekadir, Karim
N1 - Funding Information:
This research has been conducted using the UK Biobank Resource under Application Number 2964. This work was partly funded by the European Union's Horizon 2020 research and innovation program under grant agreement number 825903 (euCanSHare project). SP acknowledges the British Heart Foundation for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource in 5000 CMR scans ( www.bhf.org.uk ; PG/14/89/31194). SP acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Barts. SP acknowledges support from and from the SmartHeart EPSRC programme grant ( www.nihr.ac.uk ; EP/P001009/1). This article was supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH). This work was supported by Health Data Research UK. ST acknowledges the support of Canon Medical and the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme (grant RCSRF1819\8\25).
Publisher Copyright:
Copyright © 2022 Campello, Xia, Liu, Sanchez, Martín-Isla, Petersen, Seguí, Tsaftaris and Lekadir.
PY - 2022/9/23
Y1 - 2022/9/23
N2 - Age has important implications for health, and understanding how age manifests in the human body is the first step for a potential intervention. This becomes especially important for cardiac health, since age is the main risk factor for development of cardiovascular disease. Data-driven modeling of age progression has been conducted successfully in diverse applications such as face or brain aging. While longitudinal data is the preferred option for training deep learning models, collecting such a dataset is usually very costly, especially in medical imaging. In this work, a conditional generative adversarial network is proposed to synthesize older and younger versions of a heart scan by using only cross-sectional data. We train our model with more than 14,000 different scans from the UK Biobank. The induced modifications focused mainly on the interventricular septum and the aorta, which is consistent with the existing literature in cardiac aging. We evaluate the results by measuring image quality, the mean absolute error for predicted age using a pre-trained regressor, and demonstrate the application of synthetic data for counter-balancing biased datasets. The results suggest that the proposed approach is able to model realistic changes in the heart using only cross-sectional data and that these data can be used to correct age bias in a dataset.
AB - Age has important implications for health, and understanding how age manifests in the human body is the first step for a potential intervention. This becomes especially important for cardiac health, since age is the main risk factor for development of cardiovascular disease. Data-driven modeling of age progression has been conducted successfully in diverse applications such as face or brain aging. While longitudinal data is the preferred option for training deep learning models, collecting such a dataset is usually very costly, especially in medical imaging. In this work, a conditional generative adversarial network is proposed to synthesize older and younger versions of a heart scan by using only cross-sectional data. We train our model with more than 14,000 different scans from the UK Biobank. The induced modifications focused mainly on the interventricular septum and the aorta, which is consistent with the existing literature in cardiac aging. We evaluate the results by measuring image quality, the mean absolute error for predicted age using a pre-trained regressor, and demonstrate the application of synthetic data for counter-balancing biased datasets. The results suggest that the proposed approach is able to model realistic changes in the heart using only cross-sectional data and that these data can be used to correct age bias in a dataset.
U2 - 10.3389/fcvm.2022.983091
DO - 10.3389/fcvm.2022.983091
M3 - Article
C2 - 36211555
SN - 2297-055X
VL - 9
SP - 983091
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
ER -