  Generative adversarial networks (GANs) have provided promising data
enrichment solutions by synthesizing high-fidelity images. However, generating
large sets of labeled images with new anatomical variations remains unexplored.
We propose a novel method for synthesizing cardiac magnetic resonance (CMR)
images on a population of virtual subjects with a large anatomical variation,
introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human
phantom. We investigate two conditional image synthesis approaches grounded on
a semantically-consistent mask-guided image generation technique: 4-class and
8-class XCAT-GANs. The 4-class technique relies on only the annotations of the
heart; while the 8-class technique employs a predicted multi-tissue label map
of the heart-surrounding organs and provides better guidance for our
conditional image synthesis. For both techniques, we train our conditional
XCAT-GAN with real images paired with corresponding labels and subsequently at
the inference time, we substitute the labels with the XCAT derived ones.
Therefore, the trained network accurately transfers the tissue-specific
textures to the new label maps. By creating 33 virtual subjects of synthetic
CMR images at the end-diastolic and end-systolic phases, we evaluate the
usefulness of such data in the downstream cardiac cavity segmentation task
under different augmentation strategies. Results demonstrate that even with
only 20% of real images (40 volumes) seen during training, segmentation
performance is retained with the addition of synthetic CMR images. Moreover,
the improvement in utilizing synthetic images for augmenting the real data is
evident through the reduction of Hausdorff distance up to 28% and an increase
in the Dice score up to 5%, indicating a higher similarity to the ground truth
in all dimensions.
