Synthetic Ultrasound

Synthetic ultrasound image generation is a rapidly developing field aiming to address the limitations of real ultrasound data, such as scarcity, variability, and annotation challenges. Current research focuses on generating realistic synthetic images using various deep learning architectures, including CycleGANs, transformers, and diffusion models, often leveraging multimodal data like CT scans or MRI for improved accuracy and anatomical detail. This approach enables the creation of large, annotated datasets for training robust deep learning models for tasks such as segmentation, view recognition, and catheter tracking, ultimately improving the accuracy and efficiency of ultrasound-based medical diagnoses and procedures.