  Medical images with specific pathologies are scarce, but a large amount of
data is usually required for a deep convolutional neural network (DCNN) to
achieve good accuracy. We consider the problem of segmenting the left
ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular
magnetic resonance (CMR) scans of which only some of the scans have scar
tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using
chained generative adversarial networks (GAN). Our novel approach factorizes
the simulation process into 3 steps: 1) a mask generator to simulate the shape
of the scar tissue; 2) a domain-specific heuristic to produce the initial
simulated scar tissue from the simulated shape; 3) a refining generator to add
details to the simulated scar tissue. Unlike other approaches that generate
samples from scratch, we simulate scar tissue on normal scans resulting in
highly realistic samples. We show that experienced radiologists are unable to
distinguish between real and simulated scar tissue. Training a U-Net with
additional scans with scar tissue simulated by ScarGAN increases the percentage
of scar pixels correctly included in LV myocardium prediction from 75.9% to
80.5%.
