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Endoscopic Ultrasound Image Synthesis Using a Cycle-Consistent Adversarial Network

Grimwood, A; Ramalhinho, J; Baum, ZMC; Montaña-Brown, N; Johnson, GJ; Hu, Y; Clarkson, MJ; ... Bonmati, E; + view all (2021) Endoscopic Ultrasound Image Synthesis Using a Cycle-Consistent Adversarial Network. In: Simplifying Medical Ultrasound. ASMUS 2021. (pp. pp. 169-178). Springer: Cham, Switzerland. Green open access

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Abstract

Endoscopic ultrasound (EUS) is a challenging procedure that requires skill, both in endoscopy and ultrasound image interpretation. Classification of key anatomical landmarks visible on EUS images can assist the gastroenterologist during navigation. Current applications of deep learning have shown the ability to automatically classify ultrasound images with high accuracy. However, these techniques require a large amount of labelled data which is time consuming to obtain, and in the case of EUS, is also a difficult task to perform retrospectively due to the lack of 3D context. In this paper, we propose the use of an image-to-image translation method to create synthetic EUS (sEUS) images from CT data, that can be used as a data augmentation strategy when EUS data is scarce. We train a cycle-consistent adversarial network with unpaired EUS images and CT slices extracted in a manner such that they mimic plausible EUS views, to generate sEUS images from the pancreas, aorta and liver. We quantitatively evaluate the use of sEUS images in a classification sub-task and assess the Fréchet Inception Distance. We show that synthetic data, obtained from CT data, imposes only a minor classification accuracy penalty and may help generalization to new unseen patients. The code and a dataset containing generated sEUS images are available at: https://ebonmati.github.io.

Type: Proceedings paper
Title: Endoscopic Ultrasound Image Synthesis Using a Cycle-Consistent Adversarial Network
ISBN-13: 9783030875824
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-87583-1_17
Publisher version: https://doi.org/10.1007/978-3-030-87583-1_17
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Endoscopic ultrasound, Synthesis, Classification
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inst for Liver and Digestive Hlth
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10137495
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