Dataset Information

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

ABSTRACT:

Background

Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied.

Methods

On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed.

Results

ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations.

Conclusions

Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

SUBMITTER: de Sitter A

PROVIDER: S-EPMC7674567 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

de Sitter Alexandra A Verhoeven Tom T Burggraaff Jessica J Liu Yaou Y Simoes Jorge J Ruggieri Serena S Palotai Miklos M Brouwer Iman I Versteeg Adriaan A Wottschel Viktor V Ropele Stefan S Rocca Mara A MA Gasperini Claudio C Gallo Antonio A Yiannakas Marios C MC Rovira Alex A Enzinger Christian C Filippi Massimo M De Stefano Nicola N Kappos Ludwig L Frederiksen Jette L JL Uitdehaag Bernard M J BMJ Barkhof Frederik F Guttmann Charles R G CRG Vrenken Hugo H

Journal of neurology 20200703 12

<h4>Background</h4>Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied.<h4>Methods</h4>On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen a ...[more]

PMID: 32621103

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Dataset Information

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

Background

Methods

Results

Conclusions

Publications

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

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