Ultrasound image simulation with generative adversarial network

Grace Pigeau; Lydia Elbatarny; Victoria Wu; Abigael Schonewille; Gabor Fichtinger; Tamas Ungi

doi:10.1117/12.2549592

16 March 2020 Ultrasound image simulation with generative adversarial network

Grace Pigeau, Lydia Elbatarny, Victoria Wu, Abigael Schonewille, Gabor Fichtinger, Tamas Ungi

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131508 (2020) https://doi.org/10.1117/12.2549592
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

PURPOSE: It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network. METHODS: To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented with ten simulated images not included in the first part of the survey and asked to evaluate the realism of the images. RESULTS: The average number of correctly identified images was 28 of 50 (56%). On a scale of 1-5, where 5 is indistinguishable from real US, the generated images received an average score of 3.75 for realistic anatomy and 4.0 for realistic ultrasound effects. CONCLUSIONS: We evaluated the realism of kidney ultrasound images generated using adversarial networks. Generative adversarial networks appear to be a promising method of simulating realistic ultrasound images from crosssectional anatomical label-maps.

Conference Presentation

Citation Download Citation

Grace Pigeau, Lydia Elbatarny, Victoria Wu, Abigael Schonewille, Gabor Fichtinger, and Tamas Ungi "Ultrasound image simulation with generative adversarial network", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131508 (16 March 2020); https://doi.org/10.1117/12.2549592

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available