Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs.

Journal: International journal of computer assisted radiology and surgery

Published Date: Aug 7, 2019

Abstract

PURPOSE: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans.

Authors

Santiago Vitale

Pladema, UNICEN, Tandil, Argentina. svitale@conicet.gov.ar.
José Ignacio Orlando

Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Emmanuel Iarussi

UTN-FRBA, Buenos Aires, Argentina.
Ignacio Larrabide

Pladema, UNICEN, Tandil, Argentina.

Keywords

Abdomen Algorithms Humans Image Processing, Computer-Assisted Machine Learning Neural Networks, Computer Tomography, X-Ray Computed Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (31392671)

Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs.

Abstract

Authors

Keywords

External Resources

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