Abstract：The segmentation of left myocardium can be used not only to calculate the volume of left ventricle, but also to evaluate the quality of myocardium, track the movement of myocardium, reconstruct the heart, therefore is appliable for clinical evaluation of cardiac function, which is of great significance for the diagnosis and treatment of myocardial infarction, hypertrophy and other cardiac diseases. Cardiac cine magnetic resonance image is widely used for left myocardial segmentation and functional evaluation, which has high temporal and spatial resolution. However, left myocardium is adjacent to tissues of similar gray scale outside, and may be connected with trabecular and papillary muscles inside, which brings great difficulty to left myocardium segmentation. Great efforts have been made in the left myocardium segmentation algorithm. This paper reviewed the progress on the left myocardial segmentation based on CCMRI. We focused on the new methods, including optical flow method and deep learning following the introduction of traditional methods and its advantages and disadvantages that were summarized and compared. At last, evaluation criteria in common were briefly introduced. In conclusion, segmentation methods based on deep learning has higher accuracy and faster speed than the traditional ones, which is worth of further investigations in the future while problems such as massive data requirements and hyper parameter determination still need to be solved in deep learning.
王慧, 王丽嘉. 基于心脏电影磁共振图像的左心肌分割新进展[J]. 中国生物医学工程学报, 2020, 39(2): 238-246.
Wang Hui, Wang Lijia. New Progress of Left Myocardial Segmentation Based on Cardiac Cine MRI. Chinese Journal of Biomedical Engineering, 2020, 39(2): 238-246.
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