CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction

作     者：Zhang, Yuqi Yu, Mengbo Tong, Chao Zhao, Yanqing Han, Jintao 

作者机构：Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peking Univ Third Hosp Dept Intervent Radiol & Vasc Surg Beijing Peoples R China 

出 版 物：《INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES》 (交叉科学：计算生命科学)

年 卷 期：2024年第16卷第1期

页      面：58-72页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

基　　金：National Natural Science Foundation of China 

主　　题：Ischemic stroke Carotid Stroke risk prediction Deep learning 3D image segmentation 

摘      要：Stroke is still the World s second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.