1. 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
- Author
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de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklos, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A., Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C., Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L., Uitdehaag, Bernard M. J., Barkhof, Frederik, Guttmann, Charles R. G., Vrenken, Hugo, Universitat Autònoma de Barcelona, de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklo, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A, Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C, Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L, Uitdehaag, Bernard M J, Barkhof, Frederik, Guttmann, Charles R G, Vrenken, Hugo, de Sitter, A., Verhoeven, T., Burggraaff, J., Liu, Y., Simoes, J., Ruggieri, S., Palotai, M., Brouwer, I., Versteeg, A., Wottschel, V., Ropele, S., Rocca, M. A., Gasperini, C., Gallo, A., Yiannakas, M. C., Rovira, A., Enzinger, C., Filippi, M., De Stefano, N., Kappos, L., Frederiksen, J. L., Uitdehaag, B. M. J., Barkhof, F., Guttmann, C. R. G., Vrenken, H., Radiology and nuclear medicine, Neurology, and Amsterdam Neuroscience - Brain Imaging
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Correlation coefficient ,Caudate nucleus ,Grey matter ,030218 nuclear medicine & medical imaging ,Multiple sclerosis ,03 medical and health sciences ,0302 clinical medicine ,Atrophy ,Automated segmentation methods ,Deep grey matter ,Medicine ,Humans ,Segmentation ,Multiple sclerosi ,Gray Matter ,Neuroradiology ,Original Communication ,business.industry ,Putamen ,Brain ,medicine.disease ,Magnetic Resonance Imaging ,medicine.anatomical_structure ,Neurology ,Brain size ,Automated segmentation method ,Neurology (clinical) ,business ,Nuclear medicine ,030217 neurology & neurosurgery ,Human - 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.
- Published
- 2020
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