Print Email Facebook Twitter Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion Title Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion Author Mulder, Inge A. (Leiden University Medical Center) Khmelinskii, Artem (Leiden University Medical Center; Percuros B.V) Dzyubachyk, Oleh (Leiden University Medical Center) de Jong, Sebastiaan (Leiden University Medical Center) Rieff, Nathalie (Leiden University Medical Center) Wermer, Marieke J.H. (Leiden University Medical Center) Hoehn, Mathias (Leiden University Medical Center; Percuros B.V; Max Planck Institute for Metabolism Research) Lelieveldt, B.P.F. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) van den Maagdenberg, Arn M.J.M. (Leiden University Medical Center) Date 2017 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. Subject Automated segmentationIschemic strokeLesionMouseMRIQuantificationVolume To reference this document use: http://resolver.tudelft.nl/uuid:71b81644-e487-4fc1-aa6b-b691e2d90de2 DOI https://doi.org/10.3389/fninf.2017.00003 ISSN 1662-5196 Source Frontiers in Neuroinformatics, 11 Part of collection Institutional Repository Document type journal article Rights © 2017 Inge A. Mulder, Artem Khmelinskii, Oleh Dzyubachyk, Sebastiaan de Jong, Nathalie Rieff, Marieke J.H. Wermer, Mathias Hoehn, B.P.F. Lelieveldt, Arn M.J.M. van den Maagdenberg Files PDF fninf_11_00003.pdf 6.52 MB Close viewer /islandora/object/uuid:71b81644-e487-4fc1-aa6b-b691e2d90de2/datastream/OBJ/view