White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual or semi-automatic delineation and intuitive localization, which is time-consuming and observer-dependent. Current automatic quantification methods focus mainly on the segmentation, but the spatial distribution of lesions plays a vital role in clinical diagnosis. In this study, we implemented four segmentation algorithms and compared the performances quantitatively and qualitatively on two open-access datasets. The location-specific analysis was conducted sequentially on 213 clinical patients with cerebral ischemia and lacune. The experimental results suggest that our deep-learning-based model has the potential to be integrated into the clinical workflow.

推测血管来源的白质磁共振高信号，可以在老年人中广泛观察到，并且在多项神经学研究中具有重要意义。定量测量通常严重依赖手动或半自动描绘和直观定位，这既耗时又依赖于观察者。目前的自动量化方法主要侧重于分割，但病变的空间分布在临床诊断中起着至关重要的作用。在这项研究中，我们实施了四种分割算法，并在两个开放访问数据集上定量和定性地比较了性能。对 213 名临床脑缺血和腔隙性脑缺血患者依次进行了位置特异性分析。