Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

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dc.contributor.authorMangalagiri, Jayalakshmi
dc.contributor.authorChapman, David
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorYesha, Yaacov
dc.contributor.authorGalita, Joshua
dc.contributor.authorMenon, Sumeet
dc.contributor.authorYesha, Yelena
dc.contributor.authorSaboury, Babak
dc.contributor.authorMorris, Michael
dc.contributor.authorNguyen, Phuong
dc.date.accessioned2021-04-29T16:14:59Z
dc.date.available2021-04-29T16:14:59Z
dc.descriptionThe 2020 International Conference on Computational Science and Computational Intelligence (CSCI) Dec. 16-18, 2020, Las Vegas, USAen_US
dc.description.abstractWe present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes. We believe conditional cGAN to be a tractable approach to generate 3D CT volumes, even though the problem of generating full resolution deep fakes is presently impractical due to GPU memory limitations. We present results for autoencoder, denoising, and depixelating tasks which are trained and tested on two novel COVID19 CT datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from 12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89 to 1.en_US
dc.description.sponsorshipThis research was supported by NSF RAPID award titled: Deep Learning Models for Early Screening of COVID-19 using CT Images, award # 2027628. Authors would like to thank Dr. Eliot Siegel for his contributions.en_US
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/9458065en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2m0bf-oaw6
dc.identifier.citationJ. Mangalagiri et al., "Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network," 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020, pp. 858-862, doi: 10.1109/CSCI51800.2020.00160.en_US
dc.identifier.urihttps://doi.org/10.1109/CSCI51800.2020.00160
dc.identifier.urihttps://mdsoar.org/handle/11603/21398
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.rights© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights© 2020 IEEE.
dc.titleToward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Networken_US
dc.typeTexten_US

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