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Updated: Mar 7, 2026
Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model
Published on: August 11, 2023
Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion
Inge A Mulder1, Artem Khmelinskii2, Oleh Dzyubachyk3
1Department of Neurology, Leiden University Medical Center Leiden, Netherlands.
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.
Insights
A new automated method accurately quantifies ischemic stroke lesion progression in mouse brains using magnetic resonance imaging (MRI). This tool speeds up analysis, making longitudinal studies more efficient.
Area of Science:
- Neuroscience
- Medical Imaging
- Biomedical Engineering
Background:
- Magnetic resonance imaging (MRI) is crucial for longitudinal studies in experimental ischemic stroke in mice.
- Quantitative analysis of MRI data is hindered by time-consuming manual segmentation of brain lesions.
Purpose of the Study:
- To develop a fully automated algorithm for analyzing longitudinal MRI data to quantify ischemic lesion volume progression in mouse brains.
- To overcome the limitations of manual tracing and thresholding techniques in mouse stroke MRI analysis.
Main Methods:
- A level-set-based lesion segmentation algorithm was developed, requiring only T2-weighted MRI sequences.
- The algorithm was validated on a diverse dataset of 121 mouse brain scans with varying parameters and time points.
- Performance was evaluated against a semi-automated ground truth, including manual correction.
Main Results:
- The automated method demonstrated good agreement with human observations for lesion segmentation.
- The algorithm proved accurate across heterogeneous data, including different ages and MRI acquisition parameters.
- Significant reduction in average execution time compared to manual methods was achieved.
Conclusions:
- A fully automated, accurate, and efficient algorithm for quantifying ischemic stroke lesion progression in mouse brains using MRI has been developed.
- The developed algorithm and associated datasets are publicly available, facilitating further research.
- This tool enhances the efficiency of longitudinal MRI studies in preclinical stroke research.
