# Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis

arXiv: https://arxiv.org/abs/2308.15088 · HTML: https://mlsanity.com/paper/2308.15088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Y-net: 3D intracranial artery segmentation using a convolutional autoencoder](https://mlsanity.com/paper/1712.07194/) (2017)
- [Cerebrovascular Network Segmentation on MRA Images with Deep Learning](https://mlsanity.com/paper/1812.01752/) (2018)

## Built on by (most cited first, 4 of 4)

- [Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario](https://mlsanity.com/paper/2411.02477/) (2024)
- [A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks](https://mlsanity.com/paper/2403.18734/) (2024)
- [Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection](https://mlsanity.com/paper/2507.02349/) (2025)
- [BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling](https://mlsanity.com/paper/2604.24999/) (2026)

## Abstract (arXiv)

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.