TY - JOUR

T1 - Segmentation of age-related white matter changes in a clinical multi-center study

AU - Dyrby, Tim B

AU - Rostrup, Egill

AU - Baaré, William F C

AU - van Straaten, Elisabeth C W

AU - Barkhof, Frederik

AU - Vrenken, Hugo

AU - Ropele, Stefan

AU - Schmidt, Reinhold

AU - Erkinjuntti, Timo

AU - Wahlund, Lars-Olof

AU - Pantoni, Leonardo

AU - Inzitari, Domenico

AU - Paulson, Olaf B

AU - Hansen, Lars Kai

AU - Waldemar, Gunhild

AU - LADIS study group

PY - 2008

Y1 - 2008

N2 - Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error.

AB - Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error.

KW - Aged

KW - Aged, 80 and over

KW - Aging

KW - Brain

KW - Humans

KW - Image Interpretation, Computer-Assisted

KW - Magnetic Resonance Imaging

KW - Neural Networks (Computer)

U2 - 10.1016/j.neuroimage.2008.02.024

DO - 10.1016/j.neuroimage.2008.02.024

M3 - Journal article

C2 - 18394928

VL - 41

SP - 335

EP - 345

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

ER -