Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
This work addresses the problem of limited data for rare CoW variations, which is important for cerebrovascular disease research and clinical interventions, though it appears incremental as it builds on existing generative models.
The authors tackled the challenge of generating diverse anatomical variations of the Circle of Willis (CoW) in brain vasculature, proposing a conditional latent diffusion model with shape guidance that achieved a 53% better FID score than the best GAN-based model.
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53\% better than the best-performing GAN-based model.