AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation
AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation
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DOI：
10.3390/sym12050721
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发表时间：
2020-05
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影响因子：
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通讯作者：
Jianxin Zhang;Xiaogang Lv;HengBo Zhang;B. Liu
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文献类型：
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作者：
Jianxin Zhang;Xiaogang Lv;HengBo Zhang;B. Liu
由于脑肿瘤的大小和形状不均匀、不规则和非结构化，因此从磁共振成像（MRI）中自动分割脑肿瘤是一项具有挑战性的任务。近年来，基于对称U-Net架构的脑肿瘤分割方法取得了较好的效果。同时，最近的研究也显示了增强局部响应在特征提取和恢复中的有效性，这可能会促进脑肿瘤分割问题的更好表现。受此启发，我们尝试将注意力机制引入到现有的U-Net架构中，以探索局部重要响应对该任务的影响。具体而言，我们提出了一种端到端的二维脑肿瘤分割网络，即注意残差U-Net (AResU-Net)，该网络将注意机制和残差单元同时嵌入到U-Net中，以进一步提高脑肿瘤分割的性能。AResU-Net在相应的下采样和上采样过程中增加了一系列注意单元，并自适应重尺度特征，有效增强了下采样残差特征的局部响应，用于后续上采样过程的特征恢复。我们在BraTS 2017和BraTS 2018数据集的两个MRI脑肿瘤分割基准上广泛评估了AResU-Net。实验结果表明，所提出的AResU-Net分割方法优于其基线，与典型的脑肿瘤分割方法具有相当的性能。
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.