# Y-net: 3D intracranial artery segmentation using a convolutional autoencoder

arXiv: https://arxiv.org/abs/1712.07194 · HTML: https://mlsanity.com/paper/1712.07194/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

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

- [MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2507.19931/) (2025)
- [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)
- [Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning](https://mlsanity.com/paper/2101.09253/) (2021)
- [A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations](https://mlsanity.com/paper/2210.02416/) (2022)
- [CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images](https://mlsanity.com/paper/2311.10224/) (2023)
- [Deep Open Snake Tracker for Vessel Tracing](https://mlsanity.com/paper/2107.09049/) (2021)

## Abstract (arXiv)

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this report, an optimized CAE model (Y-net) was trained to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. The trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.