title: Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion.
abstract: Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.
authors.affiliation: Department of Neurology, Leiden University Medical Center Leiden, Netherlands.
authors.forename: Inge A
authors.initials: IA
authors.lastname: Mulder
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical CenterLeiden, Netherlands; Percuros B.V.Enschede, Netherlands.
authors.forename: Artem
authors.initials: A
authors.lastname: Khmelinskii
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center Leiden, Netherlands.
authors.forename: Oleh
authors.initials: O
authors.lastname: Dzyubachyk
authors.affiliation: Department of Human Genetics, Leiden University Medical Center Leiden, Netherlands.
authors.forename: Sebastiaan
authors.initials: S
authors.lastname: de Jong
authors.affiliation: Department of Human Genetics, Leiden University Medical Center Leiden, Netherlands.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Rieff
authors.affiliation: Department of Neurology, Leiden University Medical Center Leiden, Netherlands.
authors.forename: Marieke J H
authors.initials: MJ
authors.lastname: Wermer
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical CenterLeiden, Netherlands; Percuros B.V.Enschede, Netherlands; In-vivo-NMR Laboratory, Max Planck Institute for Metabolism ResearchCologne, Germany.
authors.forename: Mathias
authors.initials: M
authors.lastname: Hoehn
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical CenterLeiden, Netherlands; Intelligent Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of TechnologyDelft, Netherlands.
authors.forename: Boudewijn P F
authors.initials: BP
authors.lastname: Lelieveldt
authors.affiliation: Department of Neurology, Leiden University Medical CenterLeiden, Netherlands; Department of Human Genetics, Leiden University Medical CenterLeiden, Netherlands.
authors.forename: Arn M J M
authors.initials: AM
authors.lastname: van den Maagdenberg
keywords: MRI; automated segmentation; ischemic stroke; lesion; mouse; quantification; volume