# Cerebrovascular Network Segmentation on MRA Images with Deep Learning

## TL;DR

This paper introduces Uception, a novel 3D convolutional neural network inspired by U-net and Inception modules, designed for cerebrovascular segmentation in MRA images, outperforming existing models.

## Contribution

The paper presents Uception, a new deep learning architecture tailored for cerebrovascular segmentation, with optimized objective functions for sparse data, achieving superior performance.

## Key findings

- Uception outperforms state-of-the-art models in cerebrovascular segmentation.
- Optimized objective functions improve segmentation accuracy on sparse MRA data.
- The architecture effectively captures complex cerebrovascular structures.

## Abstract

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.