Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
COMPARISON OF GENERATIVE ADVERSARIAL NETWORKS IN MEDICAL IMAGING APPLICATIONS - MR to CT image synthesis
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cancer is one of the leading causes of death worldwide with about half of all cancer patients undergoing radiation therapy, either as a standalone treatmentor in combination with other methods such as chemotherapy. ‘The dose planning of radiation therapy is based on medical images such as CT and MR images. A recent trend is to move towards “MR-only” workƒows, and previous work have shown good results when synthesizing CT images from MR with machine learning methods. Eliminating the need for CT scans in the dose planning procedure removes a potentially carcinogenic part of the procedure, saves clinical resources, and could shorten the time until treatment can begin.

Th‘is thesis builds upon earlier work, where a Cycle-Consistent Adversarial Network (CycleGAN) was used successfully to synthesize CT images from MR Iimages. We compare the CycleGAN architecture to the original Generative Adversarial Network (GAN), and a variant called Wasserstein GAN (WGAN). Th‘e UNetwas used as a generative sub-network for all models.Th‘e CycleGAN utilized a PatchGAN discriminative sub-network while the other models used a custom Convolutional Neural Network (CNN). Th‘e GAN architecture was tested both with paired and unpaired data. Th‘e best results were obtained by the GAN using unpaired data, with an MAE of 30:4 HU and PSNR of 26:7 dB. ‘The CycleGAN model failed to produce images what could pass as suitable synthetic CT images. We could not reproduce the results of earlier work in this regard while either using the hyperp arameters of previous studies or other con€gurations. But we conclude that it is possible to produce synthesized CT images using GANs with both paired and unpaired data.

Place, publisher, year, edition, pages
2019. , p. 41
Series
UMNAD ; 1222
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-165184OAI: oai:DiVA.org:umu-165184DiVA, id: diva2:1369923
External cooperation
Västerbottenens läns landsting
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2020-02-03 Created: 2019-11-13 Last updated: 2020-02-03Bibliographically approved

Open Access in DiVA

fulltext(6061 kB)32 downloads
File information
File name FULLTEXT01.pdfFile size 6061 kBChecksum SHA-512
06ed545e0db27b8cfa528be13bd215df1a4224c8d93505d6422bdee8502bc1bef2c1e00b57b67fe318426c05e79a6835c3b27d340ffaf8d7f32ddab32fbc67b1
Type fulltextMimetype application/pdf

By organisation
Department of Computing Science
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 32 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 204 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf