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 (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Verhoeven, Tom (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Burggraaff, Jessica (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Neurology, MS Center Amsterdam)
Liu, Yaou (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Simoes, Jorge (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Ruggieri, Serena (San Camillo Forlanini Hospital)
Palotai, Miklos (Brigham and Women's Hospital (Boston, Estats Units d'Amèrica))
Brouwer, Iman (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Versteeg, Adriaan (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Wottschel, Viktor (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Ropele, Stefan (Medical University of Graz. Department of Neurology)
Rocca, Mara A. (IRCCS San Raffaele Scientific Institute (Milà, Itàlia). Neurology Unit)
Gasperini, Claudio (San Camillo Forlanini Hospital)
Gallo, Antonio (University of Campania "Luigi Vanvitelli". Division of Neurology and MRI Research Center, Department of Medical, Surgical, Neurologic, Metabolic and Aging Sciences)
Yiannakas, Marios C. (University College London. Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology)
Rovira, Alex (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Enzinger, Christian (University of Graz. Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology Medical)
Filippi, Massimo (Vita-Salute San Raffaele University)
De Stefano, Nicola (University of Siena. Department of Neurological and Behavioural Sciences)
Kappos, Ludwig (University Hospital, Kantonsspital. Department of Neurology)
Frederiksen, Jette L. (Glostrup University Hospital Copenhagen)
Uitdehaag, Bernard M. J. (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Neurology, MS Center Amsterdam)
Barkhof, Frederik (University College London. Institutes of Neurology & Healthcare Engineering)
Guttmann, Charles R. G. (Brigham and Women's Hospital (Boston, Estats Units d'Amèrica))
Vrenken, Hugo (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam)
Universitat Autònoma de Barcelona

Data:	2020
Resum:	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. 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. 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. 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. The online version of this article (10. 1007/s00415-020-10023-1) contains supplementary material, which is available to authorized users.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Multiple sclerosis ; Deep grey matter ; Atrophy ; Automated segmentation methods
Publicat a:	Journal of Neurology, Vol. 267 (july 2020) , p. 3541-3554, ISSN 1432-1459

DOI: 10.1007/s00415-020-10023-1
PMID: 32621103

14 p, 1.7 MB

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