# Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net

arXiv: https://arxiv.org/abs/1811.04312 · HTML: https://mlsanity.com/paper/1811.04312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://mlsanity.com/paper/2201.12785/) (2022)

## Abstract (arXiv)

Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated ap-proach using 2D deep residual dilated networks which captures rich context information of different tissues for the segmentation of eight brain structures. The proposed system was evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22 teams. We further compared the method with traditional U-Net using leave-one-subject-out cross-validation setting on the public dataset. Experimental results shows that the proposed method outperforms traditional U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35mm vs 11.59mm in averaged robust Hausdorff distance) and is computationally efficient.