Data from: Automated ischemic lesion segmentation in MRI mouse brain data after transient middle cerebral artery occlusion

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.磁共振成像（Magnetic resonance imaging，MRI）在小鼠缺血性脑卒中实验中的应用价值日益凸显，尤其适用于纵向研究。然而，磁共振成像数据的定量分析仍存在诸多挑战，其核心难点在于小鼠脑部缺血病灶的分割高度依赖耗时的人工描记与阈值分割技术。为此，本研究开发了一种全自动分析方法，用于处理纵向磁共振成像数据，以量化小鼠脑部缺血病灶的体积进展情况。本研究提出一种基于水平集的病灶分割算法，该算法基于最小化假设集构建，且仅需单序列磁共振成像（T2）作为输入。为验证该算法的有效性，本研究采用了一组异质性数据集，包含121份来自不同年龄组、梗死诱导后不同时间点的小鼠脑部扫描图像，且这些图像均由不同磁共振成像硬件及采集参数获取。本研究将自动化分割方法得到的缺血病灶的体积准确度与区域重叠度，与通过半自动化流程（包含极为耗时的人工校正步骤）获取的金标准进行了对比评估。结果表明，该算法与人工标注结果具有良好的一致性，在异质性数据集上仍可保持较高精度，且平均运行时间大幅缩短。本研究开发的算法已被编译为工具箱并公开发布，相关数据集亦同步公开。