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Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

  • Ilja ManakovEmail author
  • Markus Rohm
  • Christoph Kern
  • Benedikt Schworm
  • Karsten Kortuem
  • Volker Tresp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model’s feature maps reveals that it has learned to distinguish retinal layers and other distinct regions of the images.

Keywords

Optical coherence tomography Generative Adversarial Networks Image denoising 

Supplementary material

490967_1_En_1_MOESM1_ESM.pdf (13.1 mb)
Supplementary material 1 (pdf 13394 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ilja Manakov
    • 1
    • 2
    Email author
  • Markus Rohm
    • 1
    • 2
  • Christoph Kern
    • 2
  • Benedikt Schworm
    • 2
  • Karsten Kortuem
    • 2
  • Volker Tresp
    • 1
    • 3
  1. 1.Chair for Database Systems and Data MiningLMU MunichMunichGermany
  2. 2.Department of OphthalmologyLMU MunichMunichGermany
  3. 3.Siemens AG, Corporate TechnologyMunichGermany

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