Department of Radiology, Weill Medical College of Cornell University, New York, NY 10022, USA.
IEEE Trans Biomed Eng. 2010 Apr;57(4):905-13. doi: 10.1109/TBME.2009.2014545. Epub 2009 Feb 6.
An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 +/- 6.2 mL (mean +/- standard deviation) and -0.9 +/- 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 +/- 6.9 mL and -1.0 +/- 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.
本文提出了一种自动左心室 (LV) 分割算法，用于在临床实践中定量心输出量和心肌质量。首先使用区域生长和迭代阈值检测到周围心肌和组织中的渗出液来分割 LV 心内膜。然后使用主动轮廓模型提取心外膜，该模型由心内膜边界和通过迭代阈值估计的心肌信号信息引导。ITHACA 算法（迭代阈值和自适应主动轮廓模型）与临床实践中的手动跟踪和商业 MASS 分析软件（通用电气）在 38 名患者中进行了比较，该研究获得了机构审查委员会（IRB）的批准。ITHACA 算法在定义心肌边界方面优于 MASS 软件。ITHACA 算法与手动跟踪的一致性较好，血容量和心肌质量的平均差异分别为 2.9 +/- 6.2 mL（平均值 +/- 标准差）和 -0.9 +/- 16.5 g。差异小于手动跟踪和 MASS 软件之间的差异（分别约为 -20.0 +/- 6.9 mL 和 -1.0 +/- 20.2 g）。这些实验结果支持所提出的 ITHACA 分割算法准确且可用于临床实践。
