3D Vessel Graph Generation Using Denoising Diffusion

• URL: http://arxiv.org/abs/2407.05842v1
• Date: Mon, 8 Jul 2024 11:39:21 GMT
• Title: 3D Vessel Graph Generation Using Denoising Diffusion
• Authors: Chinmay Prabhakar, Suprosanna Shit, Fabio Musio, Kaiyuan Yang, Tamaz Amiranashvili, Johannes C. Paetzold, Hongwei Bran Li, Bjoern Menze,
• Abstract summary: Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation. 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. We introduce the first application of textitdenoising diffusion models in 3D vessel graph generation.
• Score: 4.100929120985704
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \textit{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.

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