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 compared with the background of the image, making it difficult to locate and identify boundary details. 2) The strength of the LA is quite similar to its surrounding chambers, the myocardial wall is thinner, the quality of medical images is not high, the resolution is limited, and the boundaries often appear blurred or missing in the LA surrounding the pulmonary vein (PV). 3) The shapes and sizes of the LA vary significantly thematically as the number and topology of the PV. Our critical review is focused on the integration of current segmentation algorithms and traditional segmentation methods, deep learning based segmentation, and traditional & deep learning-integrated segmentation. Traditional segmentation methods are mainly composed of the active contour model (ACM), atlas segmentation and threshold issue. ACM requires an accurate initial contour. Atlas segmentation requires complete multiple atlas sets and atlas registration, but the manual annotation of atlas sets is a challenging task due to a large number of atlas sets, which makes manual annotation difficult to be completed. In addition, the result of the annotation is vulnerable to be influenced by different taggers and atlas registration is very time-consuming. The threshold method requires the pre-determination of an appropriate threshold, which may be subjective and could ultimately limit the applicability and reproducibility. Although the traditional segmentation methods have achieved certain results, the accuracy of the segmentation is still insufficient. In recent years, deep learning technique has shown its potentials in medical image analysis, and they have qualified in different imaging modes and different clinical applications. It has improved imaging efficiency and quality, image analysis and interpretation and clinical evaluation. With the development of convolutional neural network (CNN), many variant CNN models have emerged, which have made great impacts on the improvement of segmentation algorithms. The full convolutional network (FCN) is a variant of the CNN. Based on the CNN, the FCN uses the 1 × 1 convolutional layer to update the full connection layer, and changes the height and width of the feature maps of the intermediate layers back to the size of the input image in terms of transposing the convolutional layer, the prediction results and the input image have one-to-one correspondence in the spatial dimension, the FCN can accept input images of any size, and generate segmentation images of the same size. The FCN mainly uses three techniques: 1) convolution, 2) upsampling and 3) skip connection. The FCN uses the skip connection structure to upsample feature maps of the last layer of the network model, and fused with feature maps of the shallow layer, combining the high-level semantic information with the low-level image information. The U-Net is a variant model of the FCN. The U-Net adopts the encoder-decoder architecture to form a U-shaped structure with four downsampling operations followed by four up sampling steps. The U-Net captures global features on the contraction path and achieves precise positioning on the extension path, thus the segmentation problem-solving of complex neuron structures has achieved excellent performance adequately. On this basis, variant models of the 3D U-Net and the V-Net are introduced. The training of neural network models requires a large amount of labeled data as there are millions of parameters in the network that need to be optimized. Accurate segmentation of the LA is of great clinical significance for the diagnosis and analysis of AF. However, manual segmentation of the LA is time-consuming and prone to human-related errors. Therefore, the research of automatic segmentation algorithms is essential in assisting diagnosis and clinical decision-making. We summarize the pros and cons of varied segmentation strategies, existing public data sets and clinical applications of atrial fibrillation analysis and its future trends.