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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：使用拓扑稳定状态阈值和区域限制动态规划在心脏MRI中进行自动左心室分割

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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：原理和目的：左心室（LV）的分割在评估心脏功能参数中非常重要。这项研究的目的是开发一种新颖而强大的算法，该算法可以提高对短轴心脏磁共振图像（MRI）进行自动LV分割的准确性。资料和方法：本研究中使用的数据库包括从Sunnybrook健康科学中心获得的45例病例。 45例包括12例缺血性心力衰竭，12例非缺血性心力衰竭，12例左室肥厚和9例正常病例。该分割算法开发了三种关键技术：1）提出了拓扑稳定状态阈值化方法来细化心内膜轮廓; 2）采用非最大值梯度抑制方法的边缘图; 3）提出了区域限制技术改善动态规划以得出心外膜边界。结果：验证实验是在45个案例的数据集上进行的。对于我们结果的心内膜和心外膜轮廓，良好轮廓的百分比约为91％，平均垂直距离约为2 mm，重叠的骰子度量约为0.91。专家和我们提出的方法对射血分数的回归系数和确定系数分别为1.05和0.9048; LV质量分别为0.98和0.8221。结论：提出了一种使用拓扑稳态阈值和区域受限动态规划的自动方法来分割短轴心脏MRI中的左心室。评价结果表明，提出的分割方法可以提高左心室分割的准确性和鲁棒性。所提出的分割方法表现出更好的性能，并且在提高心血管疾病计算机辅助诊断系统的准确性方面具有巨大潜力。

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