面向房颤分析的左心房分割方法综述  

Advances of left atrial segmentation methods for atrial fibrillation analysis

作　　者：赵春艳 吴清 余太慧[2] 蔡兆熙[2] 沈君[2] 赵地 郭士杰 王元全 Zhao Chunyan;Wu Qing;Yu Taihui;Cai Zhaoxi;Shen Jun;Zhao Di;Guo Shijie;Wang Yuanquan(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China;Department of Radiology,Sun Yat-sen Memorial Hospital,Sun Yat-sen University,Guangzhou 510120,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401 [2]中山大学孙逸仙纪念医院放射科,广州510120 [3]中国科学院计算技术研究所,北京100190

出　　处：《中国图象图形学报》2022年第12期3429-3449,共21页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61976241,61871173)。

摘　　要：房颤是一种起源于心房的心脏疾病。据估计全球有超过3 000万人受其影响,虽然通过治疗可以降低患病风险,但房颤通常是隐匿的,很难及时诊断和干预。房颤的诊断方法主要有心脏触诊、光学体积描记术、血压监测振动法、心电图和基于影像的方法。房颤类型主要为阵发性房颤,前4种诊断方法不一定能捕捉到房颤发作,而且诊断周期长、成本高、准确率低及容易受医生的影响。左心房的解剖结构为房颤病理和研究进展提供了重要信息,基于医学影像的房颤分析需要准确分割左心房,通过分割结果计算房颤的临床指标,例如,射血分数、左心房体积、左心房应变及应变率,然后对左心房功能进行定量评估。采用影像的方法得出的诊断结果不易受人为干扰且具有处理大批量患者数据的能力,辅助医生及早发现房颤,对患者进行干预治疗,提高对房颤症状和临床诊断的认识,在临床实践中具有重大意义。本文将已有的分割方法归纳为传统方法、基于深度学习的方法以及传统与深度学习结合的方法。这些方法得到的结果为后续房颤分析提供了依据,但目前的分割方法许多都是半自动的,分割结果不够精确,训练数据集较小且依赖手工标注。本文总结了各种方法的优缺点,归纳了目前已有的公开数据集和房颤分析的临床应用,并展望了未来的发展趋势。Atrial fibrillation(AF) is one of the most arrhythmia symptoms nowadays. The incidence rate of AF increases with elder growth and it can reach 10% population over 75 years old. The AF duration can be divided into paroxysmal, persistent and permanent, and it is induced to the morbidity and mortality of cardiovascular diseases severely. It affects more than 30 million people worldwide like reducing the quality of life and linking high risk of cerebral infarction and death. Although the risk can be reduced with appropriate treatment, AF is often latent and difficult to diagnose and intervene quickly. Recent AF-diagnostic methods have composed of cardiac palpation, optical plethysmography, blood pressure monitoring and vibration, electrocardiogram(ECG) and image-based methods. Most of atrial fibrillation has paroxysmal atrial fibrillation. The four diagnostic methods mentioned above may not capture the onset of atrial fibrillation. It is challenged for long-term diagnosis cycles, high costs, low accuracy and vulnerability. Medical imaging promotes contemporary modern medicine, computed tomography(CT) and magnetic resonance imaging(MRI) via transparent image of the cardiac anatomy. The MRI can be as one of the key medical imaging techniques, which of being unaffected by ionizing radiation, having high soft tissue contrast and high spatial resolution. Current images have limited of low signal-to-noise ratio(SNR) and low resolution to a certain extent. AF is regarded as a heart disease of atrial origin. In order to quantify the morphological and pathological changes of the left atrium(LA), it is necessary to segment the LA derived from the medical image. The medical imaging analysis of AF requires accurate LA-related segmentation and quantitative evaluation of the function. The segmentation and functional evaluation of the LA is crucial to improving our understanding and diagnosis of AF. However, segmentation of the LA on medical images is still being challenged. 1) The LA can occupy a small proportion of the image only

关 键 词：房颤(AF) 医学图像 深度学习(DL) 左心房分割 左心房功能 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]