Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods aga
- PDF / 1,849,369 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 24 Downloads / 172 Views
ORIGINAL COMMUNICATION
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 Alexandra de Sitter1 · Tom Verhoeven1 · Jessica Burggraaff2 · Yaou Liu1 · Jorge Simoes1 · Serena Ruggieri3,4 · Miklos Palotai5 · Iman Brouwer1 · Adriaan Versteeg1 · Viktor Wottschel1 · Stefan Ropele6 · Mara A. Rocca7,8 · Claudio Gasperini4 · Antonio Gallo9 · Marios C. Yiannakas10 · Alex Rovira11 · Christian Enzinger12 · Massimo Filippi7,8,13,14 · Nicola De Stefano15 · Ludwig Kappos16 · Jette L. Frederiksen17 · Bernard M. J. Uitdehaag2 · Frederik Barkhof1,18 · Charles R. G. Guttmann5 · Hugo Vrenken1 · the MAGNIMS Study Group Received: 4 May 2020 / Revised: 22 June 2020 / Accepted: 23 June 2020 © The Author(s) 2020
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. Keywords Multiple sclerosis · Deep grey matter · Atrophy · Automated segmentation methods
Introduction In multiple sclerosis (MS), atrophy of deep grey matter (DGM) structures like the caudate nucleus (caudate), putamen and thalamus is associated with cognitive and clinical
Alexandra de Sitter and Tom Verhoeven contributed equally. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00415-020-10023-1) contains supplementary material, which is available to authorized users. * Alexandra de Sitter [email protected] Extended author information available on the last page of the article
impairment [1–4]. Accurate segmentations of these structures from structural MRI are key to understanding these a
Data Loading...
