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Publication Detail
Endoscopic Ultrasound Image Synthesis Using a Cycle-Consistent Adversarial Network
  • Publication Type:
    Conference
  • Authors:
    Grimwood A, Ramalhinho J, Baum ZMC, Montaña-Brown N, Johnson GJ, Hu Y, Clarkson MJ, Pereira SP, Barratt DC, Bonmati E
  • Publication date:
    21/09/2021
  • Pagination:
    169, 178
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    12967 LNCS
  • ISBN-13:
    9783030875824
  • Status:
    Published
  • Name of conference:
    Simplifying Medical Ultrasound Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021
  • Print ISSN:
    0302-9743
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.
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