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

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 col...

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Detalles Bibliográficos
Autores principales: Kuang, Hulin, Wan, Wenfang, Wang, Yahui, Wang, Jie, Qiu, Wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953344/
https://www.ncbi.nlm.nih.gov/pubmed/36830780
http://dx.doi.org/10.3390/biomedicines11020243
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author Kuang, Hulin
Wan, Wenfang
Wang, Yahui
Wang, Jie
Qiu, Wu
author_facet Kuang, Hulin
Wan, Wenfang
Wang, Yahui
Wang, Jie
Qiu, Wu
author_sort Kuang, Hulin
collection PubMed
description 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.
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spelling pubmed-99533442023-02-25 Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network Kuang, Hulin Wan, Wenfang Wang, Yahui Wang, Jie Qiu, Wu Biomedicines Article 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. MDPI 2023-01-17 /pmc/articles/PMC9953344/ /pubmed/36830780 http://dx.doi.org/10.3390/biomedicines11020243 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kuang, Hulin
Wan, Wenfang
Wang, Yahui
Wang, Jie
Qiu, Wu
Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title_full Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title_fullStr Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title_full_unstemmed Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title_short Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network
title_sort automated collateral scoring on ct angiography of patients with acute ischemic stroke using hybrid cnn and transformer network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953344/
https://www.ncbi.nlm.nih.gov/pubmed/36830780
http://dx.doi.org/10.3390/biomedicines11020243
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