# Cerebrovascular Network Segmentation on MRA Images with Deep Learning

arXiv: https://arxiv.org/abs/1812.01752 · HTML: https://mlsanity.com/paper/1812.01752/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction](https://mlsanity.com/paper/2307.11603/) (2023)
- [Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis](https://mlsanity.com/paper/2308.15088/) (2023)
- [Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2209.06353/) (2022)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [Cerebrovascular Segmentation via Vessel Oriented Filtering Network](https://mlsanity.com/paper/2210.08868/) (2022)
- [Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning](https://mlsanity.com/paper/2404.01765/) (2024)

## Abstract (arXiv)

Deep learning has been shown to produce state of the art results in many tasks in biomedical imaging, especially in segmentation. Moreover, segmentation of the cerebrovascular structure from magnetic resonance angiography is a challenging problem because its complex geometry and topology have a large inter-patient variability. Therefore, in this work, we present a convolutional neural network approach for this problem. Particularly, a new network topology inspired by the U-net 3D and by the Inception modules, entitled Uception. In addition, a discussion about the best objective function for sparse data also guided most choices during the project. State of the art models are also implemented for a comparison purpose and final results show that the proposed architecture has the best performance in this particular context.