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Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 ; : 858-862, 2020.
Article in English | Scopus | ID: covidwho-1393668
ABSTRACT
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 range from 0.89 to 1. © 2020 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 Year: 2020 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 Year: 2020 Document Type: Article