Curiale Ariel H, Haak Alexander, Vegas-Sánchez-Ferrero Gonzalo, Ren Ben, Aja-Fernández Santiago, Bosch Johan G
Laboratorio de Procesado de Imagen, ETS Ingenieros de Telecomunicación, Universidad de Valladolid, Valladolid, Spain; Thoraxcenter Biomedical Engineering, Erasmus University Medical Center, Rotterdam, The Netherlands.
Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, The Netherlands.
Ultrasound Med Biol. 2014 Dec;40(12):2868-84. doi: 10.1016/j.ultrasmedbio.2014.07.014. Epub 2014 Oct 11.
Most automated segmentation approaches to the mitral valve and left ventricle in 3-D echocardiography require a manual initialization. In this article, we propose a fully automatic scheme to initialize a multicavity segmentation approach in 3-D transesophageal echocardiography by detecting the left ventricle long axis, the mitral valve and the aortic valve location. Our approach uses a probabilistic and structural tissue classification to find structures such as the mitral and aortic valves; the Hough transform for circles to find the center of the left ventricle; and multidimensional dynamic programming to find the best position for the left ventricle long axis. For accuracy and agreement assessment, the proposed method was evaluated in 19 patients with respect to manual landmarks and as initialization of a multicavity segmentation approach for the left ventricle, the right ventricle, the left atrium, the right atrium and the aorta. The segmentation results revealed no statistically significant differences between manual and automated initialization in a paired t-test (p > 0.05). Additionally, small biases between manual and automated initialization were detected in the Bland-Altman analysis (bias, variance) for the left ventricle (-0.04, 0.10); right ventricle (-0.07, 0.18); left atrium (-0.01, 0.03); right atrium (-0.04, 0.13); and aorta (-0.05, 0.14). These results indicate that the proposed approach provides robust and accurate detection to initialize a multicavity segmentation approach without any user interaction.
三维超声心动图中大多数用于二尖瓣和左心室的自动分割方法都需要手动初始化。在本文中，我们提出了一种全自动方案，通过检测左心室长轴、二尖瓣和主动脉瓣的位置，在三维经食管超声心动图中初始化多腔分割方法。我们的方法使用概率和结构组织分类来找到二尖瓣和主动脉瓣等结构；使用圆的霍夫变换来找到左心室的中心；并使用多维动态规划来找到左心室长轴的最佳位置。为了进行准确性和一致性评估，我们对19名患者的手动标记以及作为左心室、右心室、左心房、右心房和主动脉多腔分割方法的初始化进行了评估。分割结果显示，在配对t检验中，手动初始化和自动初始化之间没有统计学上的显著差异（p>0.05）。此外，在Bland-Altman分析中，检测到手动初始化和自动初始化之间在左心室（-0.04，0.10）、右心室（-0.07，0.18）、左心房（-0.01，0.03）、右心房（-0.04，0.13）和主动脉（-0.05，0.14）之间存在小的偏差。这些结果表明，所提出的方法提供了强大而准确的检测，无需任何用户交互即可初始化多腔分割方法。
