Paper Info
Title
Weakly-Supervised High-Fidelity Ultrasound Video Synthesis with Feature Decoupling
Abstract
Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-fidelity US videos. Specifically, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are three-fold. First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection. These keypoints then provide vital information for handling complex high dynamic motions in US videos. Second, we decouple content and texture learning using the dual decoders to effectively reduce the model learning difficulty. Last, we adopt the adversarial training strategy with GAN losses for further improving the sharpness of the generated videos, narrowing the gap between real and synthesis videos. We validate our method on a large in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics and user study prove the effectiveness of our proposed method.
Year
DOI
Venue
2022
10.1007/978-3-031-16440-8_30
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV
DocType
Volume
ISSN
Conference
13434
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
11
Name
Order
Citations
PageRank
Jiamin Liang100.34
Xin Yang2799.59
Yuhao Huang303.72
Kai Liu400.34
Xinrui Zhou500.34
Xindi Hu602.03
Zehui Lin752.82
Huanjia Luo800.34
Yuanji Zhang901.69
Yi Xiong1002.37
Dong Ni1113720.07