Deo, Y., Dou, H., Ravikumar, N. et al. (2 more authors) (2024) Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature. In: Deep Generative Models. Deep Generative Models workshop at 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), 08-12 Oct 2023, Vancouver, Canada. Lecture Notes in Computer Science, 14533 . Springer , pp. 164-173. ISBN 978-3-031-53766-0
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-53767-7_16 . |
Keywords: | Image Synthesis, Deep Learning, Brain Vasculature, Vessel Synthesis, Diffusion, Latent Diffusion |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering CiET1819\19 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2023 14:51 |
Last Modified: | 20 Feb 2025 01:13 |
Published Version: | https://link.springer.com/chapter/10.1007/978-3-03... |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-031-53767-7_16 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202016 |