OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound
  Video Synthesis
Han Zhou Dong Ni Ao Chang Xinrui Zhou Rusi Chen Yanlin Chen Lian Liu Jiamin Liang Yuhao Huang Tong Han Zhe Liu Deng-Ping Fan Xin Yang
Ultrasound (US) imaging is indispensable in clinical practice. To diagnose
certain diseases, sonographers must observe corresponding dynamic anatomic
structures to gather comprehensive information. However, the limited
availability of specific US video cases causes teaching difficulties in
identifying corresponding diseases, which potentially impacts the detection
rate of such cases. The synthesis of US videos may represent a promising
solution to this issue. Nevertheless, it is challenging to accurately animate
the intricate motion of dynamic anatomic structures while preserving image
fidelity. To address this, we present a novel online feature-decoupling
framework called OnUVS for high-fidelity US video synthesis. Our highlights can
be summarized by four aspects. First, we introduced anatomic information into
keypoint learning through a weakly-supervised training strategy, resulting in
improved preservation of anatomical integrity and motion while minimizing the
labeling burden. Second, to better preserve the integrity and textural
information of US images, we implemented a dual-decoder that decouples the
content and textural features in the generator. Third, we adopted a
multiple-feature discriminator to extract a comprehensive range of visual cues,
thereby enhancing the sharpness and fine details of the generated videos.
Fourth, we constrained the motion trajectories of keypoints during online
learning to enhance the fluidity of generated videos. Our validation and user
studies on in-house echocardiographic and pelvic floor US videos showed that
OnUVS synthesizes US videos with high fidelity.