Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingGenerative Adversarial NetworkGPUSSIMSimilar Papers 제목 키워드 기반
Deep Generative Model-Based Generation of Synthetic Individual-Specific Brain MRI Segmentations
To the best of our knowledge, all existing methods that can generate synthetic brain magnetic resonance imaging (MRI) scans for a specific individual require detailed structural or volumetric information about the indivi…
Generative Adversarial NetworkImproved $α$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning
Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiatio…
DiagnosticSynthetic Data GenerationProperty-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks
This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin section images. The key innovation lies in…
3D ReconstructionSynthetic Traffic Generation with Wasserstein Generative Adversarial Networks
Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic g…
Intelligent CommunicationSynthetic Data GenerationTowards Audio to Scene Image Synthesis using Generative Adversarial Network
Humans can imagine a scene from a sound. We want machines to do so by using conditional generative adversarial networks (GANs). By applying the techniques including spectral norm, projection discriminator and auxiliary c…
Generative Adversarial NetworkImage Generation