A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke

分割 人工智能 卷积神经网络 深度学习 组内相关 冲程(发动机) 医学 置信区间 Sørensen–骰子系数 计算机科学 人工神经网络 机器学习 模式识别(心理学) 内科学 图像分割 心理测量学 工程类 临床心理学 机械工程
作者
Ting Fang,Naijia Liu,Shengdong Nie,Shouqiang Jia,Xiaodan Ye
出处
期刊:Journal of X-ray Science and Technology [IOS Press]
卷期号:: 1-14
标识
DOI:10.3233/xst-230119
摘要

BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. OBJECTIVE: We propose a method combining d eep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS. METHODS: Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region. RESULTS: The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96). CONCLUSIONS: This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.
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