Cast suppression in radiographs by generative adversarial networks.

Autor:	Hržić F; Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Rijeka, Croatia.; Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia., Žužić I; Department of Informatics, Technical University of Munich, Munich, Germany., Tschauner S; Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria., Štajduhar I; Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Rijeka, Croatia.; Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2021 Nov 25; Vol. 28 (12), pp. 2687-2694.
DOI:	10.1093/jamia/ocab192
Abstrakt:	Injured extremities commonly need to be immobilized by casts to allow proper healing. We propose a method to suppress cast superimpositions in pediatric wrist radiographs based on the cycle generative adversarial network (CycleGAN) model. We retrospectively reviewed unpaired pediatric wrist radiographs (n = 9672) and sampled them into 2 equal groups, with and without cast. The test subset consisted of 718 radiographs with cast. We evaluated different quadratic input sizes (256, 512, and 1024 pixels) for U-Net and ResNet-based CycleGAN architectures in cast suppression, quantitatively and qualitatively. The mean age was 11 ± 3 years in images containing cast (n = 4836), and 11 ± 4 years in castless samples (n = 4836). A total of 5956 X-rays had been done in males and 3716 in females. A U-Net 512 CycleGAN performed best (P ≤ .001). CycleGAN models successfully suppressed casts in pediatric wrist radiographs, allowing the development of a related software tool for radiology image viewers. (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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