Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames

Abstract

Cardiac anatomy and function are interrelated in many ways, and these relations can be affected by multiple pathologies. In particular, this applies to ventricular shape and mechanical deformation. We propose a machine learning approach to capture these interactions by using a conditional Generative Adversarial Network (cGAN) to predict cardiac deformation from individual Cardiac Magnetic Resonance (CMR) frames, learning a deterministic mapping between end-diastolic (ED) to end-systolic (ES) CMR short-axis frames. We validate the predicted images by quantifying the difference with real images using mean squared error (MSE) and structural similarity index (SSIM), as well as the Dice coefficient between their respective endo- and epicardial segmentations, obtained with an additional U-Net. We evaluate the ability of the network to learn “healthy” deformations by training it on $\sim$33,500 image pairs from $\sim$12,000 subjects, and testing on a separate test set of $\sim$4,500 image pairs from the UK Biobank study. Mean MSE, SSIM and Dice scores were 0.0026 ± 0.0013, 0.89 ± 0.032 and 0.89 ± 0.059 respectively. We subsequently re-trained the network on specific patient group data, showing that the network is capable of extracting physiologically meaningful differences between patient populations suggesting promising applications on pathological data.