Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1–2.25 fps, ,  mm, and  mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques.

中文翻译：

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基于YOLOv3模型的超声心动图左心室自动分割实现约束定位

心血管疾病 (CVD) 是最常见的疾病类型，在人类中死亡率很高。早期诊断对心血管疾病的预后至关重要。在使用心肌组织应变、应变率等指标评估和分析心脏功能之前，左心室（LV）心内膜的准确分割对于保证后续诊断的准确性至关重要。为了准确分割LV心内膜，本文提出了基于YOLOv3模型的LV区域特征提取，定位LV的心尖和底部位置，以及LV区域的位置；之后可以得到LV的子图像，基于马尔可夫随机场（MRF）模型，可以实现对LV子图像心肌的初步识别和二值化。最后，在上述三个 LV 位置的约束下，可以使用非线性最小二乘曲线拟合和边缘逼近来实现 LV 心内膜的精确分割和提取。实验表明，该方法提出的分割评估指标，包括计算速度（fps）、Dice、平均绝对距离（MAD）和豪斯多夫距离（HD），可以达到2.1-2.25 fps，,  毫米，和  毫米，分别。这表明所建议的方法比现有技术具有更好的分割精度和鲁棒性。