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

04/02/2021
by   Jayalakshmi Mangalagiri, et al.
26

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.

READ FULL TEXT
research
01/22/2020

Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features

Computer-Aided-Diagnosis (CADx) systems assist radiologists with identif...
research
10/14/2021

CT-SGAN: Computed Tomography Synthesis GAN

Diversity in data is critical for the successful training of deep learni...
research
10/15/2021

Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network

Local tissue expansion of the lungs is typically derived by registering ...
research
09/06/2023

Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, whi...
research
03/03/2023

Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network

Medical imaging plays a significant role in detecting and treating vario...
research
08/10/2022

Evaluating Generatively Synthesized Diabetic Retinopathy Imagery

Publicly available data for the training of diabetic retinopathy classif...
research
11/16/2019

3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement

We propose GAN-based image enhancement models for frequency enhancement ...

Please sign up or login with your details

Forgot password? Click here to reset