Medical Image Synthesis via Deep Learning

Biting Yu, Yan Wang, Lei Wang, Dinggang Shen, Luping Zhou

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Medical images have been widely used in clinics, providing visual representations of under-skin tissues in human body. By applying different imaging protocols, diverse modalities of medical images with unique characteristics of visualization can be produced. Considering the cost of scanning high-quality single modality images or homogeneous multiple modalities of images, medical image synthesis methods have been extensively explored for clinical applications. Among them, deep learning approaches, especially convolutional neural networks (CNNs) and generative adversarial networks (GANs), have rapidly become dominating for medical image synthesis in recent years. In this chapter, based on a general review of the medical image synthesis methods, we will focus on introducing typical CNNs and GANs models for medical image synthesis. Especially, we will elaborate our recent work about low-dose to high-dose PET image synthesis, and cross-modality MR image synthesis, using these models.

Original languageEnglish
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer
Pages23-44
Number of pages22
DOIs
Publication statusPublished - 2020 Jan 1
Externally publishedYes

Publication series

NameAdvances in Experimental Medicine and Biology
Volume1213
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Keywords

  • Brain
  • Convolutional neural networks (CNNs)
  • Deep learning
  • Generative adversarial networks (GANs)
  • Machine learning
  • Magnetic resonance imaging (MRI)
  • Medical image synthesis
  • Positron emission tomography (PET)

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Yu, B., Wang, Y., Wang, L., Shen, D., & Zhou, L. (2020). Medical Image Synthesis via Deep Learning. In Advances in Experimental Medicine and Biology (pp. 23-44). (Advances in Experimental Medicine and Biology; Vol. 1213). Springer. https://doi.org/10.1007/978-3-030-33128-3_2