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

doi:10.1117/12.2581911

15 February 2021 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

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115982F (2021) https://doi.org/10.1117/12.2581911
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Mélanie Donnez, François-Xavier Carton, Florian Le Lann, Emmanuel De Schlichting, and Matthieu Chabanas "Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115982F (15 February 2021); https://doi.org/10.1117/12.2581911

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