VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel
  Synthesis
Paula Feldman Miguel Fainstein Viviana Siless Claudio Delrieux Emmanuel Iarussi
We present a data-driven generative framework for synthesizing blood vessel
3D geometry. This is a challenging task due to the complexity of vascular
systems, which are highly variating in shape, size, and structure. Existing
model-based methods provide some degree of control and variation in the
structures produced, but fail to capture the diversity of actual anatomical
data. We developed VesselVAE, a recursive variational Neural Network that fully
exploits the hierarchical organization of the vessel and learns a
low-dimensional manifold encoding branch connectivity along with geometry
features describing the target surface. After training, the VesselVAE latent
space can be sampled to generate new vessel geometries. To the best of our
knowledge, this work is the first to utilize this technique for synthesizing
blood vessels. We achieve similarities of synthetic and real data for radius
(.97), length (.95), and tortuosity (.96). By leveraging the power of deep
neural networks, we generate 3D models of blood vessels that are both accurate
and diverse, which is crucial for medical and surgical training, hemodynamic
simulations, and many other purposes.