Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences | |
Fei Liu; Dan Liu; Jie Tian; Xiaoyan Xie; Xin Yang; Wang K(王坤) | |
刊名 | Medical Image Analysis |
2020 | |
卷号 | 65期号:65页码:101793 |
关键词 | Ultrasound sequence Respiratory motion estimation Cascaded Siamese network One-shot deformable convolution |
ISSN号 | 1361-8415 |
DOI | 10.1016/j.media.2020.101793 |
通讯作者 | Wang, Kun(kun.wang@ia.ac.cn) |
英文摘要 | Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately and robustly. In this study, we propose a novel deep learning model, called a Cascaded One-shot Deformable Convolutional Neural Network (COSD-CNN), to track landmarks in real time in long ultrasound sequences. Specifically, we design a cascaded Siamese network structure to improve the tracking performance of CNN-based methods. We propose a one-shot deformable convolution module to enhance the robustness of the COSD-CNN to appearance variation in a meta-learning manner. Moreover, we design a simple and efficient unsupervised strategy to facilitate the network's training with a limited number of medical images, in which many corner points are selected from raw ultrasound images to learn network features with high generalizability. The proposed COSD-CNN has been extensively evaluated on the public Challenge on Liver UltraSound Tracking (CLUST) 2D dataset and on our own ultrasound image dataset from the First Affiliated Hospital of Sun Yat-sen University (FSYSU). Experiment results show that the proposed model can track a target through an ultrasound sequence with high accuracy and robustness. Our method achieves new state-of-the-art performance on the CLUST 2D benchmark set, indicating its strong potential for application in clinical practice. |
资助项目 | Ministry of Science and Technology of China[2017YFA0205200] ; Ministry of Science and Technology of China[2017YFA0700401] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81827808] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] |
WOS关键词 | TIME TUMOR-TRACKING ; LIVER |
WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:000567865900012 |
资助机构 | Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/41463] |
专题 | 自动化研究所_中国科学院分子影像重点实验室 |
通讯作者 | Fei Liu |
作者单位 | 1.Department of the Artificial Intelligence Technology, University of Chinese Academy of Sciences, Beijing 10 0 049, China 2.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China 3.Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China 4.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
推荐引用方式 GB/T 7714 | Fei Liu,Dan Liu,Jie Tian,et al. Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences[J]. Medical Image Analysis,2020,65(65):101793. |
APA | Fei Liu,Dan Liu,Jie Tian,Xiaoyan Xie,Xin Yang,&Wang K.(2020).Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences.Medical Image Analysis,65(65),101793. |
MLA | Fei Liu,et al."Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences".Medical Image Analysis 65.65(2020):101793. |
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