Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network

Abstract

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.