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Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Authors :
Jessica Burggraaff
Yao Liu
Juan C. Prieto
Jorge Simoes
Alexandra de Sitter
Serena Ruggieri
Iman Brouwer
Birgit I. Lissenberg-Witte
Mara A. Rocca
Paola Valsasina
Stefan Ropele
Claudio Gasperini
Antonio Gallo
Deborah Pareto
Jaume Sastre-Garriga
Christian Enzinger
Massimo Filippi
Nicola De Stefano
Olga Ciccarelli
Hanneke E. Hulst
Mike P. Wattjes
Frederik Barkhof
Bernard M.J. Uitdehaag
Hugo Vrenken
Charles R.G. Guttmann
Source :
NeuroImage: Clinical, Vol 29, Iss , Pp 102549- (2021)
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

Background and rationale: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values

Details

Language :
English
ISSN :
22131582
Volume :
29
Issue :
102549-
Database :
Directory of Open Access Journals
Journal :
NeuroImage: Clinical
Publication Type :
Academic Journal
Accession number :
edsdoj.f2ede02938334165a2f7c4caea4c0296
Document Type :
article
Full Text :
https://doi.org/10.1016/j.nicl.2020.102549