SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Heckemann Rolf A.)
 

Sökning: WFRF:(Heckemann Rolf A.) > (2015) > Robust whole-brain ...

  • Ledig, C. (författare)

Robust whole-brain segmentation: Application to traumatic brain injury

  • Artikel/kapitelEngelska2015

Förlag, utgivningsår, omfång ...

  • Elsevier BV,2015

Nummerbeteckningar

  • LIBRIS-ID:oai:gup.ub.gu.se/215142
  • https://gup.ub.gu.se/publication/215142URI
  • https://doi.org/10.1016/j.media.2014.12.003DOI

Kompletterande språkuppgifter

  • Språk:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas-Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.

Ämnesord och genrebeteckningar

  • MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin hsv//swe
  • MEDICAL AND HEALTH SCIENCES Clinical Medicine hsv//eng
  • Traumatic brain injury
  • Magnetic resonance imaging
  • Multi-atlas segmentation
  • Brain image
  • MULTI-ATLAS SEGMENTATION
  • COST FUNCTION MASKING
  • MR-IMAGES
  • AXONAL
  • INJURY
  • VENTRICLE SEGMENTATION
  • NONRIGID REGISTRATION
  • DIFFUSION-TENSOR
  • LABEL FUSION
  • CLASSIFICATION
  • HIPPOCAMPUS
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  • Interdisciplinary Applications
  • Engineering
  • Biomedical
  • Radiology
  • Nuclear Medicine & Medical Imaging

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Heckemann, Rolf A.Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology(Swepub:gu)xhecro (författare)
  • Hammers, A. (författare)
  • Lopez, Juan CarlosGothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology (författare)
  • Newcombe, V. F. J. (författare)
  • Makropoulos, A. (författare)
  • Lotjonen, J. (författare)
  • Menon, D. K. (författare)
  • Rueckert, D. (författare)
  • Göteborgs universitetInstitutionen för neurovetenskap och fysiologi (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Medical Image Analysis: Elsevier BV21:1, s. 40-581361-8415

Internetlänk

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy