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
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Vise andre og tillknytning
2015 (engelsk)Inngår i: Computational Intelligence and Neuroscience, ISSN 1687-5265, E-ISSN 1687-5273, Vol. 2015, artikkel-id 813696Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

sted, utgiver, år, opplag, sider
Hindawi Publishing Corporation, 2015. Vol. 2015, artikkel-id 813696
HSV kategori
Forskningsprogram
Medicinsk teknologi
Identifikatorer
URN: urn:nbn:se:kth:diva-179944DOI: 10.1155/2015/813696ISI: 000366408500001Scopus ID: 2-s2.0-84950116370OAI: oai:DiVA.org:kth-179944DiVA, id: diva2:890858
Forskningsfinansiär
Swedish Research Council, 2014-6153Swedish Heart Lung Foundation, 20130625
Merknad

QC 20160112. QC 20160113

Tilgjengelig fra: 2016-01-05 Laget: 2016-01-05 Sist oppdatert: 2017-12-01bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopusPublisher's website

Personposter BETA

Smedby, ÖrjanWang, Chunliang

Søk i DiVA

Av forfatter/redaktør
Smedby, ÖrjanWang, Chunliang
Av organisasjonen
I samme tidsskrift
Computational Intelligence and Neuroscience

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 207 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