Realistic synthesis of brain tumor resection ultrasound images with a Generative Adversarial Network

The simulation of realistic ultrasound (US) images has many applications in image-guided surgery such as image registration, data augmentation, or educational purposes. In this paper we simulated intraoperative US images of the brain after tumor resection surgery. In a first stage, a Generative Adversarial Networks generated an US image with resection from a resection cavity map. While the cavity texture can be realistic, surrounding structures are usually not anatomically coherent. Thus, a second stage blended the generated cavity texture into a real patient-specific US image acquired before resection. A validation study on 68 images of 21 cases showed that three raters correctly identified 64% of all images. In particular, 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.