Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear

We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the...

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Vydáno v:	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Ročník 11070; s. 3
Hlavní autoři:	Wang, Jianing, Zhao, Yiyuan, Noble, Jack H, Dawant, Benoit M
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Germany 01.01.2018
Témata:	Algorithms Cochlea - diagnostic imaging Humans Metals Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Sensitivity and Specificity Tomography, X-Ray Computed Metal Artifact Reduction Conditional Generative Adversarial Networks Cochlear Implants
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