Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium
文献类型：期刊论文
| 作者 | Zhao, Chenji5; Xiang, Shun5; Wang, Yuanquan5; Cai, Zhaoxi4; Shen, Jun4; Zhou, Shoujun3; Zhao, Di2; Su, Weihua5; Guo, Shijie1,5; Li, Shuo6 |
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2023-03-15 |
| 卷号 | 214页码:9 |
| ISSN号 | 0957-4174 |
| 关键词 | 3D MRI Contextual information Image segmentation Semi -supervised learning Transformers |
| DOI | 10.1016/j.eswa.2022.119105 |
| 英文摘要 | Accurate, robust and automatic segmentation of the left atrium (LA) in magnetic resonance images (MRI) is of great significance for studying the LA structure and facilitating the diagnosis and treatment of atrial fibrillation. Semi-supervised learning has attracted great attention in medical image segmentation, since it alleviates the heavy burden of annotating training data. In this paper, we propose a context-aware network called CA-Net for semi-supervised LA segmentation from 3D MRI. The information of 3D MRI to be learned is not only the contextual information in each slice, but also the spatial information among different slices of the data, which is not sufficiently exploit by existing methods. In the proposed CA-Net, a Trans-V module is coined from both Transformers and V-Net, which is able to learn contextual information in 3D MRI. In the training processing, the discriminator with attention mechanisms is introduced to calculate an adversarial loss so that a large amount of unlabeled data can be utilized. Experimental results on the Atrial Segmentation Challenge dataset show that the contextual information is helpful to extract more accurate atrial structures, and the proposed CA-Net achieves better performance than some SOTA semi-supervised networks. Our method achieves dice scores of 88.14% and 90.09% in segmentation results when trained with 10% and 20% of labeled data, respectively. Code will be available at: https://github.com/RhythmI/CA-Net-master. |
| 资助项目 | National Key Research and Development Program of China[2018YFA0704102] ; National Science Foundation of China (NSFC)[61976241] ; National Science Foundation of China (NSFC)[81827805] ; National Science Foundation of China (NSFC)[61871173] ; National Science Foundation of China (NSFC)[91948303] ; Basic Research Project of Shenzhen Science and Technology Innovation Commission[JCYJ20200109114610201] ; Basic Research Project of Shenzhen Science and Technology Innovation Commission[JCYJ20200109114812361] ; Shenzhen Engineering Laboratory for Interventional Key Technology of Diagnosis and Treatment with Surgical Robot ; Tianjin Science and Technology Planning Project[19ZXJRGX00080] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000883786000011 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19850]  |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Wang, Yuanquan; Zhou, Shoujun |
| 作者单位 | 1.HeBUT, Hebei Key Lab Robot Percept & Human Robot Interact, Tianjin 300401, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 4.Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Radiol, Guangzhou 510120, Peoples R China 5.Hebei Univ Technol HeBUT, Sch Artificial Intelligence, Tianjin 300401, Peoples R China 6.Western Univ, Dept Med Imaging, DIG London, London, ON N6A 4V2, Canada |
| 推荐引用方式 GB/T 7714 | Zhao, Chenji,Xiang, Shun,Wang, Yuanquan,et al. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,214:9. |
| APA | Zhao, Chenji.,Xiang, Shun.,Wang, Yuanquan.,Cai, Zhaoxi.,Shen, Jun.,...&Li, Shuo.(2023).Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium.EXPERT SYSTEMS WITH APPLICATIONS,214,9. |
| MLA | Zhao, Chenji,et al."Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium".EXPERT SYSTEMS WITH APPLICATIONS 214(2023):9. |
入库方式： OAI收割
来源：计算技术研究所
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