Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University - Kingsville, MSC 192, 700 University Blvd., Kingsville, TX, 78363-8202, USA.
Med Biol Eng Comput. 2019 Jun;57(6):1213-1228. doi: 10.1007/s11517-019-01952-9. Epub 2019 Jan 28.
A rapid method for left and right ventricular endocardial volume segmentation and clinical cardiac parameter calculation from MRI images of cardiac patients is presented. The clinical motivation is providing cardiologists a tool for assessing the cardiac function in a patient through the left ventricular endocardial volume's ejection fraction. A new method combining adapted fuzzy membership-based c-means pixel clustering and connected regions component labeling is used for automatic segmentation of the left and right ventricular endocardial volumes. This proposed pixel clustering with labeling approach avoids manual initialization or user intervention and does not require specifying the region of interest. This method fully automatically extracts the left and right ventricular endocardial volumes and avoids manual tracing on all MRI image frames in the complete cardiac cycle from systole to diastole. The average computational processing time per frame is 0.6 s, making it much more efficient than deformable methods, which need several iterations for the evolution of the snake or contour. Accuracy of the automated method presented herein was validated against manual tracing-based extraction, performed with the guidance of cardiac experts, on several MRI frames. Dice coefficients between the proposed automatic versus manual traced ventricular endocardial volume segmentations were observed to be 0.9781 ± 0.0070 (for left ventricular endocardial volume) and 0.9819 ± 0.0058 (for right ventricular endocardial volume), and the Pearson correlation coefficients were observed to be 0.9655 ± 0.0206 (for left ventricular endocardial volume) and 0.9870 ± 0.0131 (for right ventricular endocardial volume). Graphical abstract The left ventricular endocardial volume segmentation methodology illustrated as a series of algorithms.
一种快速的方法，用于从心脏患者的 MRI 图像中分割左心室和右心室心内膜体积并计算临床心脏参数。临床动机是为心脏病专家提供一种通过左心室心内膜容积射血分数评估患者心脏功能的工具。一种新的方法，结合了自适应模糊隶属度 c-均值像素聚类和连通区域分量标记，用于自动分割左心室和右心室心内膜体积。这种提出的像素聚类与标记方法避免了手动初始化或用户干预，并且不需要指定感兴趣区域。该方法完全自动提取左心室和右心室心内膜体积，并避免在整个心动周期（从收缩期到舒张期）的所有 MRI 图像帧上进行手动跟踪。每帧的平均计算处理时间为 0.6s，比需要几个迭代来演化蛇或轮廓的可变形方法效率高得多。本文提出的自动方法的准确性通过手动跟踪提取进行了验证，手动跟踪提取是在心脏专家的指导下进行的，在几个 MRI 帧上进行了验证。观察到自动与手动跟踪心室心内膜体积分割之间的 Dice 系数分别为 0.9781±0.0070（用于左心室心内膜体积）和 0.9819±0.0058（用于右心室心内膜体积），Pearson 相关系数分别为 0.9655±0.0206（用于左心室心内膜体积）和 0.9870±0.0131（用于右心室心内膜体积）。
