# Blood Vessel Geometry Synthesis using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1804.04381 · HTML: https://mlsanity.com/paper/1804.04381/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 13 of 13)

- [VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis](https://mlsanity.com/paper/2307.03592/) (2023)
- [3D Vessel Graph Generation Using Denoising Diffusion](https://mlsanity.com/paper/2407.05842/) (2024)
- [VesselGPT: Autoregressive Modeling of Vascular Geometry](https://mlsanity.com/paper/2505.13318/) (2025)
- [TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields](https://mlsanity.com/paper/2403.08974/) (2024)
- [Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation](https://mlsanity.com/paper/2506.19455/) (2025)
- [Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel](https://mlsanity.com/paper/2507.15223/) (2025)
- [Vector Representations of Vessel Trees](https://mlsanity.com/paper/2506.11163/) (2025)
- [VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation](https://mlsanity.com/paper/2603.18797/) (2026)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation](https://mlsanity.com/paper/2507.13404/) (2025)
- [Sparse Representation Learning for Vessels](https://mlsanity.com/paper/2605.01382/) (2026)
- [A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods](https://mlsanity.com/paper/2112.09809/) (2021)
- [Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling](https://mlsanity.com/paper/2506.14914/) (2025)

## Abstract (arXiv)

Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications. We propose a deep generative model to synthesize blood vessel geometries, with an application to coronary arteries in cardiac CT angiography (CCTA). In the proposed method, a Wasserstein generative adversarial network (GAN) consisting of a generator and a discriminator network is trained. While the generator tries to synthesize realistic blood vessel geometries, the discriminator tries to distinguish synthesized geometries from those of real blood vessels. Both real and synthesized blood vessel geometries are parametrized as 1D signals based on the central vessel axis. The generator can optionally be provided with an attribute vector to synthesize vessels with particular characteristics. The GAN was optimized using a reference database with parametrizations of 4,412 real coronary artery geometries extracted from CCTA scans. After training, plausible coronary artery geometries could be synthesized based on random vectors sampled from a latent space. A qualitative analysis showed strong similarities between real and synthesized coronary arteries. A detailed analysis of the latent space showed that the diversity present in coronary artery anatomy was accurately captured by the generator. Results show that Wasserstein generative adversarial networks can be used to synthesize blood vessel geometries.