• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Øvrige samlinger
  • Publikasjoner fra CRIStin - NTNU
  • Vis innførsel
  •   Hjem
  • Øvrige samlinger
  • Publikasjoner fra CRIStin - NTNU
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Ultrasound speckle reduction using generative adversial networks

Dietrichson, Fabian Sødal; Smistad, Erik; Østvik, Andreas; Løvstakken, Lasse
Journal article, Peer reviewed
Accepted version
Thumbnail
Åpne
08579764.pdf (663.1Kb)
Permanent lenke
http://hdl.handle.net/11250/2588227
Utgivelsesdato
2018
Metadata
Vis full innførsel
Samlinger
  • Institutt for sirkulasjon og bildediagnostikk [1083]
  • Institutt for teknisk kybernetikk [2275]
  • Publikasjoner fra CRIStin - NTNU [21845]
Originalversjon
Proceedings - IEEE Ultrasonics Symposium. 2018, .   10.1109/ULTSYM.2018.8579764
Sammendrag
Generative adversial networks (GANs) have shown its ability to create realistic and accurate image-to-image transformation. The goal of this work was to investigate whether deep convolutional GANs can learn to perform advanced ultrasound speckle reduction in real-time. The GAN was trained using a dataset of cardiac images from 200 patients and tested on a separate dataset from 55 patients. A U-net type of generator was used together with a patch-wise discriminator. Three different generator sizes were tested in order to see the tradeoff between speckle reduction accuracy and runtime. The results show that GANs can learn ultrasound speckle reduction. Even though the training set consisted only of cardiac ultrasound images, results from other parts of the body and scanners indicate that the method learns speckle reduction in general, and not just for cardiac images. By reducing the number of filters in the generator, real-time performance was achieved with an average of 11 ms per frame.
Utgiver
Institute of Electrical and Electronics Engineers (IEEE)
Tidsskrift
Proceedings - IEEE Ultrasonics Symposium

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit