| ZHANG Zhong（张重）*,LV Shijie*,LIU Shuang*,XIAO Baihua**.[J].高技术通讯(英文),2022,28(3):247~251 |
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| Completed attention convolutional neural network for MRI image segmentation |
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| DOI：10.3772/j.issn.1006-6748.2022.03.003 |
| 中文关键词: |
| 英文关键词: magnetic resonance imaging (MRI) image segmentation, completed attention convolutional neural network (CACNN) |
| 基金项目: |
| Author Name | Affiliation | | ZHANG Zhong（张重）* | (*Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, P.R.China)
(**State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | LV Shijie* | (*Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, P.R.China)
(**State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | LIU Shuang* | (*Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, P.R.China)
(**State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | XIAO Baihua** | (*Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, P.R.China)
(**State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Attention mechanism combined with convolutional neural network (CNN) achieves promising performance for magnetic resonance imaging (MRI) image segmentation, however these methods only learn attention weights from single scale, resulting in incomplete attention learning. A novel method named completed attention convolutional neural network (CACNN) is proposed for MRI image segmentation. Specifically, the channel-wise attention block (CWAB) and the pixel-wise attention block (PWAB) are designed to learn attention weights from the aspects of channel and pixel levels. As a result, completed attention weights are obtained, which is beneficial to discriminative feature learning. The method is verified on two widely used datasets (HVSMR and MRBrainS), and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods. |
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