Supporting data for "Using deep learning to quantify total scores of cerebral small vessel disease (CSVD) in a stroke cohort

Cerebral small vessels include the arterioles, capillaries, and venules in the brain, which are essential for controlling cerebral blood flow and maintaining brain homeostasis. Diseases related to these vessels are defined as cerebral small vessel disease (CSVD). CSVD has multiple clinical presentations, including cognitive impairment, gait disturbance, and acute ischemic stroke or transient ischemic attack. MRI features of CSVD include recent small subcortical infarct (RSSI), lacunes, enlarged perivascular space (EPVS), white matter hyperintensities (WMH), and cerebral microbleeds (CMB).
To evaluate the total CSVD load in patients, a score is applied to assess each subtype of CSVD. However, it is very tedious and time-consuming to label these by hand. This project proposed an auto pipeline for the computer-assisted detection of CSVD using deep learning on a large dataset of local stroke patients. A total number of 974 subjects—all of whom had been clinically diagnosed with transient ischemic attack or ischemic stroke—were recruited in this study. An external testing cohort comprising 48 stroke patients was also collected for this study. These patients all underwent scanning at the local MRI unit and all patients were well-informed about this research and provided signed consent. All the MRI data were processed under the standards of the local MRI unit.This thesis represents the first attempt to employ deep learning methods for the automated detection of total CSVD scores in a comparatively large cohort of stroke patients. The dataset utilized encompasses all subtypes of CSVD and was meticulously labeled by experienced clinical practitioners. This project provides a thorough and detailed application of deep learning detection on medical images and potentially opens avenues for robust applications in the field of AI-medicine.

脑小血管包括大脑中的小动脉、毛细血管和小静脉，它们对于调控脑血流和维持脑内稳态至关重要。与这些血管相关的疾病被定义为脑小血管病（cerebral small vessel disease, CSVD）。CSVD具有多种临床表现，包括认知障碍、步态异常以及急性缺血性卒中或短暂性脑缺血发作。CSVD的MRI特征包括近期皮层下小梗死（recent small subcortical infarct, RSSI）、腔隙灶、扩大的血管周围间隙（enlarged perivascular space, EPVS）、白质高信号（white matter hyperintensities, WMH）和脑微出血（cerebral microbleeds, CMB）。
为评估患者的总CSVD负荷，研究采用评分法对CSVD各亚型进行评估。然而，人工标注这些特征极为繁琐且耗时。本项目基于本地卒中患者的大型数据集，提出了一种利用深度学习实现CSVD计算机辅助检测的自动化流程。研究共招募974名受试者，均为临床确诊的短暂性脑缺血发作或缺血性卒中患者；同时收集了包含48名卒中患者的外部测试队列。所有患者均在本地MRI单元接受扫描，且均已充分了解本研究内容并签署知情同意书。所有MRI数据均按照本地MRI单元的标准进行处理。本研究首次尝试在较大规模的卒中患者队列中，采用深度学习方法实现总CSVD评分的自动化检测。所用数据集涵盖CSVD所有亚型，且由经验丰富的临床医师进行了细致标注。本项目为深度学习在医学影像检测中的应用提供了全面且详尽的范例，有望为人工智能医学领域的稳健应用开辟新路径。