Affiliations: [a] School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| [b] Jinan People’s Hospital affiliated to Shandong First Medical University, Shandong, China
| [c] Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| [d] Shanghai Institute of Medical Imaging, Shanghai, China
| [e] Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
Correspondence:
[*]
Corresponding author. Shengdong Nie, School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jun-Gong Road, Shanghai, 200093, China. E-mail: [email protected] and Shouqiang Jia, Department of Imaging, Jinan People’s Hospital affiliated to Shandong First Medical University, Shandong, 271100, China. E-mail: [email protected] and Xiaodan Ye, Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China. E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: 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 deep 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.
Keywords: Alberta stroke program early CT score, Acute ischemic stroke, Deep learning, Computer-aided method, CT angiography
