Synthetic Ultrasound Video Generation with Generative Adversarial Networks

Ultrasound is a non-invasive, radiation-free portable imaging modality that offers real-time diagnostics in different clinical settings.Ultrasound video analysis can benefit from the rise of AI-powered applications in healthcare.However, access to ultrasound data remains the main challenge for the development of state-of-the-art machine learning models.Synthetic data generation can provide various benefits to ultrasound imaging analysis.Namely, generating ultrasound videos with specific characteristics allows for better training and testing of machine learning models.This work proposes a generative adversarial network for conditional ultrasound video generation.We conduct a thorough quantitative and qualitative evaluation of the network.Additionally, we show the added value of using the synthetic video for data augmentation in a downstream task.Extensive experiments on the EchoNet-Dynamic dataset demonstrate that the proposed model achieves an FID score of 126 and an FVD score of 233 and can be used in data augmentation tasks in small data scenarios.Contents 7 Conclusion and Future Work 70 vi List of Tables 3.1 Prior work in ultrasound image and video generation with GANs. .5.1 Configuration of the image discriminator. . . . . . . . . . . . . . . .5.2 Configuration of the video discriminator. . . . . . . . . . . . . . . .5.3 Configuration of the transposed convolutional generator. . . . . . .5.4 Configuration of the subpixel generator. . . . . . .