title: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.
abstract: The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.
authors.affiliation: icometrix, R&D, Leuven, Belgium.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Jain
authors.affiliation: icometrix, R&D, Leuven, Belgium ; Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: Diana M
authors.initials: DM
authors.lastname: Sima
authors.affiliation: icometrix, R&D, Leuven, Belgium.
authors.forename: Annemie
authors.initials: A
authors.lastname: Ribbens
authors.affiliation: Department of Neurology, Center for Neurosciences, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussel, Belgium.
authors.forename: Melissa
authors.initials: M
authors.lastname: Cambron
authors.affiliation: icometrix, R&D, Leuven, Belgium.
authors.forename: Anke
authors.initials: A
authors.lastname: Maertens
authors.affiliation: icometrix, R&D, Leuven, Belgium.
authors.forename: Wim
authors.initials: W
authors.lastname: Van Hecke
authors.affiliation: Department of Neurology, Center for Neurosciences, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussel, Belgium.
authors.forename: Johan
authors.initials: J
authors.lastname: De Mey
authors.affiliation: Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Frederik
authors.initials: F
authors.lastname: Barkhof
authors.affiliation: Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Martijn D
authors.initials: MD
authors.lastname: Steenwijk
authors.affiliation: Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands ; Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Marita
authors.initials: M
authors.lastname: Daams
authors.affiliation: Department of Electrical Engineering-ESAT, PSI Medical Image Computing, KU Leuven, Leuven, Belgium ; Medical IT, iMinds, Leuven, Belgium.
authors.forename: Frederik
authors.initials: F
authors.lastname: Maes
authors.affiliation: Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium ; Medical IT, iMinds, Leuven, Belgium.
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
authors.affiliation: Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands ; Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Hugo
authors.initials: H
authors.lastname: Vrenken
authors.affiliation: icometrix, R&D, Leuven, Belgium.
authors.forename: Dirk
authors.initials: D
authors.lastname: Smeets
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D066127:White Matter / Q000473:pathology*
keywords: Brain segmentation; Magnetic resonance imaging; Multiple sclerosis; White matter lesions