title: Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling.
abstract: Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.
authors.affiliation: Dept. of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Sergi
authors.initials: S
authors.lastname: Valverde
authors.affiliation: Dept. of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Dept. of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Eloy
authors.initials: E
authors.lastname: Roura
authors.affiliation: Magnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Spain Architecture and Technology, University of Girona, Spain.
authors.forename: Deborah
authors.initials: D
authors.lastname: Pareto
authors.affiliation: Girona Magnetic Resonance Center, Spain.
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.affiliation: Multiple Sclerosis and Neuro-immunology Unit, Dr. Josep Trueta University Hospital, Spain.
authors.forename: Lluís
authors.initials: L
authors.lastname: Ramió-Torrentà
authors.affiliation: Neurology Unit, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron University Hospital, Spain.
authors.forename: Jaume
authors.initials: J
authors.lastname: Sastre-Garriga
authors.affiliation: Neurology Unit, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron University Hospital, Spain.
authors.forename: Xavier
authors.initials: X
authors.lastname: Montalban
authors.affiliation: Magnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Spain Architecture and Technology, University of Girona, Spain.
authors.forename: Àlex
authors.initials: À
authors.lastname: Rovira
authors.affiliation: Dept. of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
mesh_terms: D001921:Brain / Q000473:pathology*; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D066127:White Matter / Q000473:pathology
keywords: Automated tissue segmentation; Brain; Brain atrophy; Lesion filling; MRI; Multiple sclerosis; White matter lesions