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首页> 外文期刊>Academic radiology >Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming
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Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming

机译：使用拓扑稳态阈值和区域限制动态规划的心脏MRI中自动左心室分割

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Rationale and Objectives: Segmentation of the left ventricle (LV) is very important in the assessment of cardiac functional parameters. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic LV segmentation on short-axis cardiac magnetic resonance images (MRI). Materials and Methods: The database used in this study consists of 45 cases obtained from the Sunnybrook Health Sciences Centre. The 45 cases contain 12 ischemic heart failures, 12 non-ischemic heart failures, 12 LV hypertrophies, and 9 normal cases. Three key techniques are developed in this segmentation algorithm: 1) topological stable-state thresholding method is proposed to refine the endocardial contour, 2) an edge map with non-maxima gradient suppression approach, and 3) a region-restricted technique that is proposed to improve the dynamic programming to derive the epicardial boundary. Results: The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epicardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm, and the overlapping dice metric is about 0.91. The regression and determination coefficient for the experts and our proposed method on the ejection fraction is 1.05 and 0.9048, respectively; they are 0.98 and 0.8221 for LV mass. Conclusions: An automatic method using topological stable-state thresholding and region restricted dynamic programming has been proposed to segment left ventricle in short-axis cardiac MRI. Evaluation results indicate that the proposed segmentation method can improve the accuracy and robust of left ventricle segmentation. The proposed segmentation approach shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.
机译：理由和目标：左心室的分割（LV）是心脏功能参数的评估非常重要。本研究的目的是开发一种新的和稳健的算法，该算法可以提高自动LV分割对短轴心脏磁共振图像（MRI）的准确性。材料与方法：在这项研究中所使用的数据库包含从森尼布鲁克健康科学中心获得45案件。 45例包含12次局部缺血性心脏故障，12个非缺血性心脏故障，12个LV hypertrophies和9正常情况。三个关键技术在此分割算法开发：提出细化心内膜轮廓，2）的边缘图与非最大值梯度抑制方法1）拓扑稳定状态阈值法，以及3）提出了一种区域限制技术以提高动态规划导出心外膜边界。结果：验证实验上的数据集的45例池进行。对于我们的研究结果的内切和心外膜轮廓，良好轮廓的百分比为约91％，的平均垂直距离为约2mm，并且所述重叠的骰子度量是约0.91。对于专家回归和决定系数和我们提出的方法的射血分数是分别为1.05和0.9048;他们是0.98和0.8221的LV质量。结论：一种自动方法，使用拓扑稳定状态阈值和区域限制动态规划已经提出段左心室短轴心脏MRI。评价结果表明，所提出的分割方法可以提高精度和强大的左心室分割。所提出的分割方法显示了更好的性能和对提高心血管疾病的计算机辅助诊断系统的准确性巨大潜力。

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