Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för radiologi, onkologi och strålningsvetenskap, Avdelningen för sjukhusfysik.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för radiologi, onkologi och strålningsvetenskap, Avdelningen för sjukhusfysik.
2013 (engelsk)Inngår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 110, nr 3, s. 308-319Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure.

sted, utgiver, år, opplag, sider
2013. Vol. 110, nr 3, s. 308-319
Emneord [en]
Segmentation, Atlas based segmentation, Deformable registration, Multi-atlas segmentation, Radiotherapy prostate, Label fusion
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-202898DOI: 10.1016/j.cmpb.2012.12.006ISI: 000319178500009OAI: oai:DiVA.org:uu-202898DiVA, id: diva2:634639
Tilgjengelig fra: 2013-07-01 Laget: 2013-07-01 Sist oppdatert: 2017-12-06bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Sjöberg, CarlAhnesjö, Anders
Av organisasjonen
I samme tidsskrift
Computer Methods and Programs in Biomedicine

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 1126 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf