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.

Autor: de Sitter A; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. A.deSitter@amsterdamumc.nl., Verhoeven T; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Burggraaff J; Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands., Liu Y; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Simoes J; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Ruggieri S; Department of Human Neurosciences, 'Sapienza' University of Rome, Rome, Italy.; Department of Neurosciences, San Camillo Forlanini Hospital, Rome, Italy., Palotai M; Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Brouwer I; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Versteeg A; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Wottschel V; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands., Ropele S; Department of Neurology, Medical University of Graz, Graz, Austria., Rocca MA; Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology, Milan, Italy.; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy., Gasperini C; Department of Neurosciences, San Camillo Forlanini Hospital, Rome, Italy., Gallo A; Division of Neurology and MRI Research Center, Department of Medical, Surgical, Neurologic, Metabolic and Aging Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy., Yiannakas MC; Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK., Rovira A; Unitat de Ressonància Magnètica (Servei de Radiologia), Hospital Universitari Vall D'Hebron, Autonomous University of Barcelona, Barcelona, Spain., Enzinger C; Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology Medical, University of Graz, Graz, Austria., Filippi M; Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology, Milan, Italy.; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Vita-Salute San Raffaele University, Milan, Italy., De Stefano N; Department of Neurological and Behavioural Sciences, University of Siena, Siena, Italy., Kappos L; Department of Neurology, University Hospital, Kantonsspital, Basel, Switzerland., Frederiksen JL; Department of Neurology, Glostrup University Hospital Copenhagen, Copenhagen, Denmark., Uitdehaag BMJ; Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands., Barkhof F; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.; Institutes of Neurology and Healthcare Engineering, UCL London, London, UK., Guttmann CRG; Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Vrenken H; Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Journal of neurology [J Neurol] 2020 Dec; Vol. 267 (12), pp. 3541-3554. Date of Electronic Publication: 2020 Jul 03.
DOI: 10.1007/s00415-020-10023-1
Abstrakt: 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.
Databáze: MEDLINE
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