11 research outputs found
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
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.Comment: Accepted for MICCAI 202
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
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.
Keywords: Vascular 3D model
Bilateral pheochromocytoma after kidney transplantation in neurofibromatosis type 1
We present the case of a 25-year-old male with a history of neurofibromatosis type 1 and bilateral pheochromocytoma
4 years after kidney transplantation that was successfully treated with simultaneous bilateral posterior retroperitoneoscopic adrenalectomy
Magneto-Optical Enhancement by Plasmon Excitations in Nanoparticle/Metal Structures
International audienc