CASIA OpenIR  > 中国科学院分子影像重点实验室
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)
Source PublicationMedical Image Analysis
ISSN1361-8415
2020
Volume65Issue:65Pages:101793
Corresponding AuthorWang, Kun(kun.wang@ia.ac.cn)
Abstract

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.

KeywordUltrasound sequence Respiratory motion estimation Cascaded Siamese network One-shot deformable convolution
DOI10.1016/j.media.2020.101793
WOS KeywordTIME TUMOR-TRACKING ; LIVER
Indexed BySCI
Language英语
Funding ProjectMinistry 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]
Funding OrganizationMinistry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
WOS Research AreaComputer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000567865900012
PublisherELSEVIER
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41463
Collection中国科学院分子影像重点实验室
Corresponding AuthorFei Liu
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.Department of the Artificial Intelligence Technology, University of Chinese Academy of Sciences, Beijing 10 0 049, 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.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
Recommended Citation
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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