TY - JOUR
T1 - Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion
AU - Mulder, Inge A.
AU - Khmelinskii, Artem
AU - Dzyubachyk, Oleh
AU - de Jong, Sebastiaan
AU - Rieff, Nathalie
AU - Wermer, Marieke J.H.
AU - Hoehn, Mathias
AU - Lelieveldt, Boudewijn P F
AU - van den Maagdenberg, Arn M.J.M.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Automated segmentation
KW - Ischemic stroke
KW - Lesion
KW - Mouse
KW - MRI
KW - Quantification
KW - Volume
UR - http://www.scopus.com/inward/record.url?scp=85015438031&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:71b81644-e487-4fc1-aa6b-b691e2d90de2
U2 - 10.3389/fninf.2017.00003
DO - 10.3389/fninf.2017.00003
M3 - Article
AN - SCOPUS:85015438031
SN - 1662-5196
VL - 11
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 3
ER -