使用混合卷积神经网络和Transformer网络对急性缺血性脑卒中患者进行CT血管造影的自动侧支评分
Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network.
作者信息
Kuang Hulin, Wan Wenfang, Wang Yahui, Wang Jie, Qiu Wu
机构信息
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
出版信息
Biomedicines. 2023 Jan 17;11(2):243. doi: 10.3390/biomedicines11020243.
Collateral scoring plays an important role in diagnosis and treatment decisions of acute ischemic stroke (AIS). Most existing automated methods rely on vessel prominence and amount after vessel segmentation. The purpose of this study was to design a vessel-segmentation free method for automating collateral scoring on CT angiography (CTA). We first processed the original CTA via maximum intensity projection (MIP) and middle cerebral artery (MCA) region segmentation. The obtained MIP images were fed into our proposed hybrid CNN and Transformer model (MPViT) to automatically determine the collateral scores. We collected 154 CTA scans of patients with AIS for evaluation using five-folder cross validation. Results show that the proposed MPViT achieved an intraclass correlation coefficient of 0.767 (95% CI: 0.68-0.83) and a Kappa of 0.6184 (95% CI: 0.4954-0.7414) for three-point collateral score classification. For dichotomized classification (good vs. non-good and poor vs. non-poor), it also achieved great performance.
侧支循环评分在急性缺血性卒中（AIS）的诊断和治疗决策中起着重要作用。大多数现有的自动化方法依赖于血管分割后的血管突出程度和数量。本研究的目的是设计一种无需血管分割的方法，用于在CT血管造影（CTA）上自动进行侧支循环评分。我们首先通过最大强度投影（MIP）和大脑中动脉（MCA）区域分割对原始CTA进行处理。将获得的MIP图像输入我们提出的混合卷积神经网络（CNN）和Transformer模型（MPViT），以自动确定侧支循环评分。我们收集了154例AIS患者的CTA扫描图像，采用五折交叉验证进行评估。结果表明，所提出的MPViT在三点侧支循环评分分类中，组内相关系数为0.767（95%CI：0.68 - 0.83），Kappa值为0.6184（95%CI：0.4954 - 0.7414）。对于二分分类（良好与非良好以及差与非差），它也取得了优异的性能。
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