English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

New tissue priors for improved automated classification of subcortical brain structures on MRI

MPS-Authors
/persons/resource/persons147461

Weiskopf,  Nikolaus
Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19617

Draganski,  Bogdan
Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

Lorio_Fresard_2016.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Lorio, S., Fresard, S., Adaszewski, S., Kherif, F., Chowdhury, R., Frackowiak, R. S., et al. (2016). New tissue priors for improved automated classification of subcortical brain structures on MRI. NeuroImage, 130, 157-166. doi:10.1016/j.neuroimage.2016.01.062.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0029-AC85-9
Abstract
Despite the constant improvement of algorithms for automated brain tissue classification, the accurate delineation of subcortical structures using magnetic resonance images (MRI) data remains challenging. The main difficulties arise from the low grey-white matter contrast of iron rich areas in T1-weighted (T1w) MRI data and from the lack of adequate priors for basal ganglia and thalamus. The most recent attempts to obtain such priors were based on cohorts with limited size that included subjects in a narrow age range, failing to account for age-related grey-white matter contrast changes. Aiming to improve the anatomical plausibility of automated brain tissue classification from T1w data, we have created new tissue probability maps for subcortical grey matter regions. Supported by atlas-derived spatial information, raters manually labelled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal grey-white matter contrast in these areas. After assessment of inter-rater variability, the new tissue priors were tested on T1w data within the framework of voxel-based morphometry. The automated detection of grey matter in subcortical areas with our new probability maps was more anatomically plausible compared to the one derived with currently available priors. We provide evidence that the improved delineation compensates age-related bias in the segmentation of iron rich subcortical regions. The new tissue priors, allowing robust detection of basal ganglia and thalamus, have the potential to enhance the sensitivity of voxel-based morphometry in both healthy and diseased brains.