基于深度学习的图像分割方法在主动脉疾病中的应用  

Application of image segmentation methods based on deep learning in aortic diseases

作　　者：何峰峰 张强[1] 杨超[2] 齐晓宇[1] 冯庆敏[1] 熊海燕 刘胜林[1] HE Fengfeng;ZHANG Qiang;YANG Chao;QI Xiaoyu;FENG Qingmin;XIONG Haiyan;LIU Shenglin(Biomedical Engineering Lab,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430022,China;Department of Vascular Surgery,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology)

机构地区：[1]华中科技大学同济医学院附属协和医院生物医学工程研究室,武汉430022 [2]华中科技大学同济医学院附属协和医院血管外科

出　　处：《临床心血管病杂志》2022年第6期449-454,共6页Journal of Clinical Cardiology

基　　金：华中科技大学同济医学院附属协和医院自由创新预研基金(No:2020xhyn018、2021xhyn091);湖北省重点实验室开放基金(No:2020fzyx001)。

摘　　要：主动脉疾病(如主动脉瘤、主动脉夹层等)严重危害患者生命健康,若患者得不到及时治疗,其后果通常是致命的。借助图像分割技术精准识别出患者病灶区域,医生可以实现对疾病的精确诊断、术前规划或术后监控等。最近深度学习在医学图像分割任务中展现出明显优势,越来越多的学者将其应用于主动脉疾病领域。本研究旨在对基于深度学习的图像分割方法在主动脉疾病中的应用进行综述。Aortic diseases,such as aortic aneurysm,aortic dissection,etc.,seriously endanger patients’health.If patients are not treated in time,the consequences are usually fatal.With image segmentation to accurately identify patients’lesion area,doctors can achieve an accurate diagnosis,preoperative planning or postoperative monitoring.Recently,deep learning has shown obvious advantages in medical image segmentation tasks.An increasing number of scholars apply deep learning to aortic diseases.This paper aims to review the application of image segmentation methods based on deep learning in aortic diseases.

关 键 词：深度学习 图像分割 主动脉疾病 

分 类 号：R543.1[医药卫生—心血管疾病]