DeepAI
Log In Sign Up

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

07/31/2017
by   Lei Bi, et al.
0

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.

READ FULL TEXT
05/29/2018

Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks

With the advent of Deep Learning (DL) techniques, especially Generative ...
04/02/2020

STAN-CT: Standardizing CT Image using Generative Adversarial Network

Computed tomography (CT) plays an important role in lung malignancy diag...
08/16/2022

Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI

Generative Adversarial Networks (GANs) are state of the art for image sy...
06/29/2022

DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks

In contemporary popular music production, drum sound design is commonly ...
08/02/2018

Physics-Based Generative Adversarial Models for Image Restoration and Beyond

We present an algorithm to directly solve numerous image restoration pro...