An automated ASPECTS method with atlas-based segmentation

Zechen Yu; Zhongping Chen; Yang Yu; Haichen Zhu; Dan Tong; Yang Chen

doi:10.1016/j.cmpb.2021.106376

An automated ASPECTS method with atlas-based segmentation

Comput Methods Programs Biomed. 2021 Oct:210:106376. doi: 10.1016/j.cmpb.2021.106376. Epub 2021 Aug 31.

Authors

Zechen Yu¹, Zhongping Chen², Yang Yu², Haichen Zhu¹, Dan Tong³, Yang Chen⁴

Affiliations

¹ Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China.
² Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
³ Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China. Electronic address: tongdan2012@126.com.
⁴ Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China. Electronic address: chenyang.list@seu.edu.cn.

PMID: 34500140
DOI: 10.1016/j.cmpb.2021.106376

Abstract

Background and purpose: As a simple and reliable systematic method to evaluate the early ischemic changes in the blood supply region of the middle cerebral artery of patients with ischemic stroke, the Alberta Stroke Program Early CT score (ASPECTS) can be used for rapid semi-quantitative evaluation of ischemic lesions, which is helpful to select potential candidates for intravenous and intra-arterial therapies, determine the thrombolytic effect and long-term prognosis. This method mainly relies on doctors' visual observation. However, due to different levels of doctor's experience, the poor inter-reader agreement may result in errors in the final ASPECTS. The purpose of this work was to propose an automated semi-quantitative method for the diagnosis of acute ischemic stroke based on non-contrast computed tomography (NCCT), to provide a reference for doctors in the diagnosis and evaluation.

Methods: NCCT data from a total of 90 patients were included for auto-ASPECTS training and testing. After preprocessing CT images, the regions of interest (ROI) for ASPECTS were labeled using atlas-based segmentation. The mean difference, mean ratio and brain density shifts (BDS) of the corresponding regions of the contralateral brain were used as the standard for quantitative analysis. The auto-ASPECTS method was developed and validated to predict early ischemic changes whose performance was evaluated by the agreement (accuracy) of predictions and consensus scores of two observers.

Results: A comparison was made among the results on mean difference, mean ratio, BDS and the combination of multiple parameters as the standard. The result of using BDS alone was relatively better than the result of using any other parameter alone or any combination of multiple parameters, and accuracy in the test set was 0.80. In the test set, accuracy with using different BDS thresholds increased by 6.67% compared with using the consistent BDS threshold. After dichotomy of auto-ASPECTS and consensus scores with the threshold of 7, the agreement of them was 83.3% and there was no significant difference between the two distributions (p = 0.344) in McNemar test.

Conclusions: The proposed auto-ASPECTS method for NCCT images can provide useful information for early diagnosis and evaluation of patients with acute ischemic stroke (AIS).

Keywords: ASPECTS; Acute ischemic stroke; Automated method; NCCT.

MeSH terms

Alberta
Brain
Brain Ischemia* / diagnostic imaging
Humans
Stroke* / diagnostic imaging
Tomography, X-Ray Computed