Paper Info
Title
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 similar to 33,500 image pairs from similar to 12,000 subjects, and testing on a separate test set of similar to 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.
Year
DOI
Venue
2019
10.1007/978-3-030-39074-7_12
STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: MULTI-SEQUENCE CMR SEGMENTATION, CRT-EPIGGY AND LV FULL QUANTIFICATION CHALLENGES
Keywords
DocType
Volume
cGANs, Image transformation, Cardiac contraction, UK Biobank
Conference
12009
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Julius Ossenberg-Engels100.34
Vicente Grau23812.23