3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis

by   Yang Zhou, et al.

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest.


page 1

page 2

page 8

page 9

page 10


CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images

Positron emission tomography (PET) is the most sensitive molecular imagi...

Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation

Chromosome karyotype analysis is of great clinical importance in the dia...

Style Separation and Synthesis via Generative Adversarial Networks

Style synthesis attracts great interests recently, while few works focus...

SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal

There are five features to consider when using generative adversarial ne...

Microstructure synthesis using style-based generative adversarial network

Work considers the usage of StyleGAN architecture for the task of micros...

Style Generation: Image Synthesis based on Coarsely Matched Texts

Previous text-to-image synthesis algorithms typically use explicit textu...

Please sign up or login with your details

Forgot password? Click here to reset