Abstract
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that correspond to an anatomy of interest is challenging. Previous methods aimed at generating vessel trees mostly in an autoregressive style and could not be applied to vessel graphs with cycles such as capillaries or specific anatomical structures such as the Circle of Willis. Addressing this gap, we introduce the first application of denoising diffusion models in 3D vessel graph generation. Our contributions include a novel, two-stage generation method that sequentially denoises node coordinates and edges. We experiment with two real-world vessel datasets, consisting of microscopic capillaries and major cerebral vessels, and demonstrate the generalizability of our method for producing diverse, novel, and anatomically plausible vessel graphs.
C. Prabhakar and S. Shit—Contributed equally.
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Acknowledgments
This work has been supported by the Helmut Horten Foundation. S. S. is supported by the GRC Travel Grant from UZH. F.M. is funded by the DIZH grant. H. B. Li is supported by an SNF postdoctoral mobility grant.
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Prabhakar, C. et al. (2024). 3D Vessel Graph Generation Using Denoising Diffusion. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15011. Springer, Cham. https://doi.org/10.1007/978-3-031-72120-5_1
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