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Stefan Traud

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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YNICL Journal 2015 Journal Article

Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST)

Michael Amann
Michaela Andělová
Armanda Pfister
Nicole Mueller-Lenke
Stefan Traud
Julia Reinhardt
Stefano Magon
Kerstin Bendfeldt

Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D MRI data sets of clinical trials in order to determine the impact of therapeutic interventions on DGM atrophy in MS.

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YNIMG Journal 2012 Journal Article

Multivariate pattern classification of gray matter pathology in multiple sclerosis

Kerstin Bendfeldt
Stefan Klöppel
Thomas E. Nichols
Renata Smieskova
Pascal Kuster
Stefan Traud
Nicole Mueller-Lenke
Yvonne Naegelin

Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1. 5T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.

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YNIMG Journal 2009 Journal Article

Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis — A longitudinal voxel-based morphometry study

Kerstin Bendfeldt
Pascal Kuster
Stefan Traud
Hanspeter Egger
Sebastian Winklhofer
Nicole Mueller-Lenke
Yvonne Naegelin
Achim Gass

Previous studies have established regional gray matter (GM) volume loss in multiple sclerosis (MS) but the relationship between development of white matter (WM) lesions and changes of regional GM volumes is unclear. The present study addresses this issue by means of voxel-based morphometry (VBM). T1-weighted three-dimensional magnetic resonance imaging (MRI) data from MS patients followed up for 12 months were analyzed using VBM. An analysis of covariance model assessed with cluster size inference (all corrected for multiple comparisons, p <0. 01) was used to compare GM volumes between baseline and follow-up while controlling for age, gender, and disease duration. Lesion burden, i. e. volumes of T1 hypointense and T2 hyperintense lesions and the number of new T2 lesions at year one, was also determined. Comparing all MS patients (n =211) longitudinally, GM volume remained unchanged during one year-follow-up. Focusing on patients with relapsing remitting MS (RRMS) (n =151), significant cortical GM volume reductions between baseline and follow-up scans were found in the anterior and posterior cingulate, the temporal cortex, and cerebellum. Within the RRMS group, those patients with increasing T2 and T1 lesion burden (n =45) showed additional GM volume loss during follow-up in the frontal and parietal cortex, and precuneus. In contrast, patients lacking an increase in WM lesion burden (n =44) did not show any significant GM changes. The present study suggests that the progression of regional GM volume reductions is associated with WM lesion progression and occurs predominantly in fronto-temporal cortical areas.

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