Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
| Autor: | Jayalakshmi Mangalagiri, Yelena Yesha, Babak Saboury, Michael A. Morris, Phuong Nguyen, Joshua Galita, Aryya Gangopadhyay, David Chapman, Sumeet Menon, Yaacov Yesha |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning medicine.diagnostic_test Image quality business.industry Computer science Noise reduction Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Computed tomography Pattern recognition Solid modeling Electrical Engineering and Systems Science - Image and Video Processing computer.software_genre Peak signal-to-noise ratio Autoencoder Machine Learning (cs.LG) Range (mathematics) Voxel medicine FOS: Electrical engineering electronic engineering information engineering Artificial intelligence business computer |
| Popis: | We 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. It is a short paper accepted in CSCI 2020 conference and is accepted to publication in the IEEE CPS proceedings |
| Databáze: | OpenAIRE |
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