AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation

Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhi...

Full description

Saved in:
Bibliographic Details
Published in:Symmetry (Basel) 2020-05, Vol.12 (5), p.721
Main Authors: Zhang, Jianxin, Lv, Xiaogang, Zhang, Hengbo, Liu, Bin
Format: Article
Language:English
Subjects:
Architecture
AResU-Net
attention mechanism
Brain
Brain cancer
brain tumor segmentation
Deep learning
Feature extraction
Image segmentation
Magnetic resonance imaging
Medical diagnosis
Medical research
MRI
Neural networks
Restoration
Sampling
Semantics
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for feature extraction and restoration has also been shown in recent works, which may encourage the better performance of the brain tumor segmentation problem. Inspired by this, we try to introduce the attention mechanism into the existing U-Net architecture to explore the effects of local important responses on this task. More specifically, we propose an end-to-end 2D brain tumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously embeds attention mechanism and residual units into U-Net for the further performance improvement of brain tumor segmentation. AResU-Net adds a series of attention units among corresponding down-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance local responses of down-sampling residual features utilized for the feature recovery of the following up-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation benchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed AResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor segmentation methods.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym12050721