# Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks

arXiv: https://arxiv.org/abs/2001.00666 · HTML: https://mlsanity.com/paper/2001.00666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection](https://mlsanity.com/paper/2204.13849/) (2022)
- [SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing](https://mlsanity.com/paper/2605.17620/) (2026)

## Abstract (arXiv)

Large enough computed tomography (CT) data sets to train supervised deep models are often hard to come by. One contributing issue is the amount of manual labor that goes into creating ground truth labels, specially for volumetric data. In this research, we train a U-net architecture at a vessel segmentation task that can be used to provide insights when treating stroke patients. We create a computational model that generates synthetic vascular structures which can be blended into unlabeled CT scans of the head. This unsupervised approached to labelling is used to pre-train deep segmentation models, which are later fine-tuned on real examples to achieve an increase in accuracy compared to models trained exclusively on a hand-labeled data set.