Data from: MRI mouse brain data of ischemic lesion 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愈发重要，尤其凭借其可开展纵向研究的优势。但对磁共振成像数据进行定量分析仍存在较大挑战，主要原因在于磁共振成像数据中小鼠脑部病变的分割高度依赖耗时的手动描记与阈值处理操作。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，以定量小鼠脑部缺血性病变的体积变化进程。我们提出了一种基于水平集的病变分割算法，该算法仅基于极少量假设构建，且仅需单序列磁共振成像（T2）作为输入。为验证该算法的性能，我们采用了一个异质性数据集，该数据集包含121份小鼠脑部扫描影像，覆盖不同年龄组、脑梗死造模后的多个时间点，且由不同的磁共振成像硬件与采集参数获取得到。我们以半自动流程获取的金标准（该流程包含极为耗时的手动校正步骤）作为参照，评估了本全自动方法分割出的缺血性病变的体积准确性与区域重叠度。结果表明，本方法与人工标注结果具有良好的一致性，在异质性数据集上具备较高的准确性，同时平均运行时间大幅缩短。本研究开发的算法已封装为工具箱并公开提供，相关数据集也一并公开。