Knowledge Commons of Institute of Automation,CAS
Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network | |
Zhao YX(赵元兴)1,3![]() ![]() ![]() ![]() | |
2019-10 | |
会议名称 | MICCAI2019 |
会议日期 | 2019-10 |
会议地点 | 深圳 |
出版者 | Springer |
摘要 | Despite remarkable progress, 3D whole brain segmentation of structural magnetic resonance imaging (MRI) into a large number of regions (>100) is still difficult due to the lack of annotated data and the limitation of GPU memory. To address these challenges, we propose a semi-supervised segmentation method based on deep neural networks to exploit the plenty of unlabeled data by extending the self-training method, and improve the U-Net model by designing a novel self-ensemble architecture and a random patch-size training strategy. Further, to reduce the model storage and computational cost, we get a compact model by knowledge distillation. Extensive experiments conducted on the MICCAI 2012 dataset demonstrate that our method dramati- cally outperforms previous methods and has achieved the state-of-the-art per- formance. Our compact model segments an MRI image within 3 s on a TITAN X GPU, which is much faster than multi-atlas based methods and previous deep learning methods. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49670 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences, 2.Brainnetome Center, Institute of Automation 3.University of Chinese Academy of Sciences 4.CAS Center for Excellence of Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhao YX,Zhang YM,Song M,et al. Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network[C]:Springer,2019. |
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