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Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03185137
Contributeur : Matthieu Chabanas <>
Soumis le : mardi 30 mars 2021 - 10:54:42
Dernière modification le : jeudi 6 mai 2021 - 08:52:01

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SPIE_MI21_11598-84_paper.pdf
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Mélanie Donnez, François-Xavier Carton, Florian Le Lann, Emmanuel de Schlichting, Matthieu Chabanas. Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network. SPIE Medical Imaging, Feb 2021, Online Only, France. pp.84, ⟨10.1117/12.2581911⟩. ⟨hal-03185137⟩

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