title: New tissue priors for improved automated classification of subcortical brain structures on MRI.
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.
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: S
authors: S
authors: Lorio
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: S
authors: S
authors: Fresard
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: S
authors: S
authors: Adaszewski
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: F
authors: F
authors: Kherif
authors: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.
authors: R
authors: R
authors: Chowdhury
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: R S
authors: RS
authors: Frackowiak
authors: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.
authors: J
authors: J
authors: Ashburner
authors: Medical Radiation Physics, Lund University Hospital, Lund, Sweden.
authors: G
authors: G
authors: Helms
authors: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
authors: N
authors: N
authors: Weiskopf
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland.
authors: A
authors: A
authors: Lutti
authors: LREN, Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne, Switzerland; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Electronic address: bogdan.draganski@chuv.ch.
authors: B
authors: B
authors: Draganski
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D055815:Young Adult
keywords: Basal ganglia; Effective transverse relaxation; Magnetization transfer saturation; Relaxometry; Tissue probability maps; Voxel-based morphometry; Voxel-based quantification