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SFCN: Symmetric feature comparison network for detecting ischemic stroke lesions on CT images
SFCN: Symmetric feature comparison network for detecting ischemic stroke lesions on CT images
Long Zhang
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Chuang Zhu
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
YueWei Wu
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Yang Yang
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Yihao Luo
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Ruoning Song
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Lian Liu
(The interventional neuroradiology department Beijing Tiantan Hospital Beijing China)
Jie Yang
(School of Artificial Intelligence, Intelligent Perception and Computing Research Center Beijing University of Posts and Telecommunications Beijing China)
Journal: IET Image Processing
•
• Wiley
• ISSN: 1751-9667
Abstract
Abstract Ischemic stroke is the most common stroke and the leading cause of disability and death in the world. Computed tomography (CT) is a popular and economical diagnostic device for the stroke, However the ischemic stroke lesions are not evident on CT images and the diagnostic result relies on the visual observation of neurologists, which may vary from doctor to doctor. To facilitate the treatment, a computer‐aided detection algorithm on CT images is proposed to help clinician for the ischemic stroke screening. In order to obtain accurate lesion annotation on CT images, novel automatic algorithms are developed to achieve image pairing, calibration, and registration. Then, a new framework with the symmetric feature extraction and comparison is proposed to identify and locate the ischemic stroke lesion. Experimental results show that this method achieves 75% of DICE in the detection of ischemic stroke lesions, which is higher than other methods by 4%. Its competitive results compared with seven latest methods is shown in terms of extensive qualitative and quantitative evaluation. This method can accurately detect the lesion in the CT images through the comparison of symmetric regional features, which has contributed to the clinical diagnosis of ischemic stroke.