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

Lorio, S; Fresard, S; Adaszewski, S; Kherif, F; Chowdhury, R; Frackowiak, R S; Ashburner, J; Helms, Gunther LU ; Weiskopf, N and Lutti, A, et al. (2016) In NeuroImage 130. p.157-166
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 gray-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 gray-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... (More)
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 gray-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 gray-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 gray matter regions. Supported by atlas-derived spatial information, raters manually labeled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal gray-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 gray 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. (Less)
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published
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NeuroImage
volume
130
pages
157 - 166
publisher
Elsevier
external identifiers
  • pmid:26854557
  • scopus:84958541870
  • wos:000372745600014
ISSN
1095-9572
DOI
10.1016/j.neuroimage.2016.01.062
language
English
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yes
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2402fa85-17ad-4a29-b4e2-fc531863d04f (old id 8829006)
date added to LUP
2016-03-04 12:29:50
date last changed
2017-08-20 03:11:37
@article{2402fa85-17ad-4a29-b4e2-fc531863d04f,
  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 gray-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 gray-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 gray matter regions. Supported by atlas-derived spatial information, raters manually labeled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal gray-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 gray 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.},
  author       = {Lorio, S and Fresard, S and Adaszewski, S and Kherif, F and Chowdhury, R and Frackowiak, R S and Ashburner, J and Helms, Gunther and Weiskopf, N and Lutti, A and Draganski, B},
  issn         = {1095-9572},
  language     = {eng},
  pages        = {157--166},
  publisher    = {Elsevier},
  series       = {NeuroImage},
  title        = {New tissue priors for improved automated classification of subcortical brain structures on MRI.},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2016.01.062},
  volume       = {130},
  year         = {2016},
}