School of Biomedical Engineering Southern Medical University, Guangzhou, China.
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Hum Brain Mapp. 2022 Jul;43(10):3023-3036. doi: 10.1002/hbm.25845. Epub 2022 Mar 31.
Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.
缺血性脑卒中是最常见的脑卒中类型，是全球范围内排名第二的致死病因。 Alberta 卒中项目早期 CT 评分（ASPECTS）被认为是一种系统的方法，用于评估急性缺血性脑卒中（AIS）患者非对比 CT 扫描（NCCT）上的缺血性改变，同时仍然需要专家经验，并且读者之间的结果也不一致。在本研究中，我们提出了一种自动 ASPECTS 方法，利用神经网络的强大学习能力客观地对 AIS 患者的 CT 扫描进行评分。首先，我们提出使用一站式卒中成像中的 CT 灌注（CTP）为 ASPECTS 评分提供缺血区域的金标准。其次，我们设计了一个不对称网络来捕捉左右两侧每个 ASPECTS 区域之间的特征，以估计其缺血状态。第三，我们在一个包含 870 名患者的大型主数据集和一个由 207 名放射科医生评分的独立测试数据集上进行了实验。实验结果表明，我们的网络取得了显著的性能，在主数据集中的灵敏度和准确率分别为 93.7%和 92.4%，在独立测试数据集的灵敏度和准确率分别为 95.5%和 91.3%。在后一个数据集中，我们的分析表明，ASPECTS 评分与 90DmRs 患者的预后之间存在高度正相关。此外，我们发现 ASPECTS 评分是梗死 CTP 核心体积大小的良好指标。该方法在 NCCT 图像上自动 ASPECTS 评分方面具有潜力。
