期刊
SYMMETRY-BASEL
卷 12, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/sym12050721
关键词
brain tumor segmentation; MRI; deep learning; attention mechanism; AResU-Net
资金
- National Key R&D Program of China [2018YFC0910506]
- National Natural Science Foundation of China [61972062]
- Natural Science Foundation of Liaoning Province [2019-MS-011]
- Key R&D Program of Liaoning Province [2019 JH2/10100030]
- Liaoning BaiQianWan Talents Program
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.
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