  Computationally synthesized blood vessels can be used for training and
evaluation of medical image analysis applications. We propose a deep generative
model to synthesize blood vessel geometries, with an application to coronary
arteries in cardiac CT angiography (CCTA).
  In the proposed method, a Wasserstein generative adversarial network (GAN)
consisting of a generator and a discriminator network is trained. While the
generator tries to synthesize realistic blood vessel geometries, the
discriminator tries to distinguish synthesized geometries from those of real
blood vessels. Both real and synthesized blood vessel geometries are
parametrized as 1D signals based on the central vessel axis. The generator can
optionally be provided with an attribute vector to synthesize vessels with
particular characteristics.
  The GAN was optimized using a reference database with parametrizations of
4,412 real coronary artery geometries extracted from CCTA scans. After
training, plausible coronary artery geometries could be synthesized based on
random vectors sampled from a latent space. A qualitative analysis showed
strong similarities between real and synthesized coronary arteries. A detailed
analysis of the latent space showed that the diversity present in coronary
artery anatomy was accurately captured by the generator.
  Results show that Wasserstein generative adversarial networks can be used to
synthesize blood vessel geometries.
