Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network
Mélanie Donnez, François-Xavier Carton, Florian Le Lann, Emmanuel De Schlichting, Matthieu Chabanas · 2021
The simulation of realistic ultrasound (US) images has many applications in image-guided surgery such as image registration, data augmentation, or education. We simulated intraoperative US images of the brain after tumor resection surgery. A Generative Adversarial Networks first generated an US image with resection from a resection cavity map. This generated cavity texture was then blended into a real pre-resection patient-specific US image. A validation study showed that two neurosurgeons correctly labelled only 56% and 53% of the simulated images, which indicate that these synthesized images are hardly distinguishable from real post-resection US images.