面对严重的医学影像分析缺口，深度学习的发展能够满足国内医疗行业的需求。心脏图像的处理方法可大致分为传统的图像处理技术、基于图谱的方法（atlas-based methods）、基于模型的方法（model-based methods）以及目前热门的采用机器学习和深度学习的方法。在深度学习兴起之前，传统的机器学习技术如模型法和图集法在心脏图像分割中有良好表现，但通常需要大量的特征工程知识或先验知识才能获得令人满意的精度。而基于深度学习的算法能从数据中自动发现复杂的特征以进行对象检测和分割。得益于先进的计算机硬件以及更多可用于训练的数据集，基于深度学习的分割算法已超越了以往的传统方法。本文回顾了2012—2022年有关心室、心外膜和心包脂肪的图像处理的各项方法、衡量指标及其目前的研究现状，并结合分割技术的发展，讨论了心脏分割的发展趋势。

Human-cardiovascular disease is challenged for its high morbidity and severe sequelae nowadays. To meet the need of the medical industry, current medical image analysis is facilitated via the development of deep learning. Conventional image processing technology is processed basically in terms of thresholding. The emerging deep learning technique can be focused on reality-oriented function in terms of specific eigenvalues. Such deep residual network and generative confrontation network have its potentials for its effectiveness and robust originated from good learning ability and data-driven factors. Our critical analysis is based on 1) characteristics of representative methods, 2) resources and scale of cardiac images, 3) comparative study of the performance evaluation and application conclusions of different methods through popular evaluation indicators, and 4) clinical domains are discussed as well. The literature review is originated from IEEE, SPIE, and China National Knowledge Network, with image processing and heart as search keywords. The difference between image processing methods is evaluated in terms of Dice coefficient and Hausdorff distance, and the performance is evaluated in quantitative further. For chamber segmentation, several approaches for right ventricle segmentation are reviewed and analyzed. As far as the principle of the segmentation method is concerned, the single of threshold is still challenged for segmentation unless it is integrated into other related methods, so it cannot be used as a single zone in segmentation technique. A brief theoretical introduction is mentioned for each method. Then, its methodology, prior datasets, and the effectiveness of segmentation process are involved in evaluation. Finally, the pros and cons of each method are analyzed as well. For the domain of epicardium and pericardium tissue, we will briefly introduce the popular image processing techniques for segmenting epicardium and pericardium tissue. Four category of key methods are analyzed in relevance to its:traditional image processing methods, atlas-based methods, machine learning, and deep learning. Traditional image processing methods are composed of such techniques of thresholding, region growing, and active contouring. Finally, Dice coefficient-derived capabilities of each algorithm are compared horizontally. For the segmentation method of the epicardium, it is easier to segment the epicardium into pericardium-illustrated coordination. Epicardial and pericardial fatty tissue are unevenly distributed around the heart, resulting in large sections-between variability and the images-between for its computed tomography (CT) and magnetic resonance imaging (MRI). The heterogeneity in shape is required to demonstrate further. However, the pericardium is featured of more smoother, thinner and oval in CT and MRI images. Such methods of active contours or ellipse fitting are suitable for segmenting such shapes naturally. Once the pericardium is divided, the epicardium is more easily divided into all pericardium-within fatty tissue. The great challenge is focused on epicardiumthinner segmentation. The slice of thickness can be set at 2~3 mm when CT scans are collected for coronary artery calcification (CAC) scoring. The pericardium is usually less than 2 mm thick, and it will often appear blurred or blurred on CT images in accordance with partial volume averaging, especially for heart organ-moving consistency. Some methods are purely pericardial delineation methods, while others are part of a method to segment and quantify the epicardium. For the epicardium part, we will mainly introduce the method of epicardium segmentation by the first pericardium-segmented. Pericardial fat segmentation methods typically rely on traditional image processing methods, such as 1) thresholding and region growing, and 2) various preprogrammed heuristics can be used to identify common structures and segment pericardial fat. Recent atlas-based segmentation approaches are employed but its clinical ability is relatively weakened. After current situation of segmentation is introduced, we will introduce some real scenarios applied in clinical practice. We can see that cardiac image processing has a large number of clinical problems are required to be solved. At the same time, we will briefly introduce the market situation of image processing in domestic market, integration of industry, education and research, and the main relevant policy trends. the development of the main related industries is introduced and involve in like 1) the establishment of related imaging databases in China, 2) the development of related imaging technologies in China, and 3) the development of related hardware equipment in China. At the end, it is discussed that the development of cardiac image segmentation processing is increasingly inseparable in related to the development of deep learning. However, because deep learning itself is difficult to be explained, we called on medical knowledge-interpreted method models, and deep learning based constraints are called to be resolved further, such homogeneity data sets and its related of higher accuracy.