Queiros Sandro, Papachristidis Alexandros, Morais Pedro, Theodoropoulos Konstantinos C, Fonseca Jaime C, Monaghan Mark J, Vilaca Joao L, Dhooge Jan
IEEE Trans Biomed Eng. 2017 Aug;64(8):1711-1720. doi: 10.1109/TBME.2016.2617401. Epub 2016 Oct 13.
A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.
提出了一种用于三维（3-D）经食管超声心动图（TEE）数据集的主动脉瓣（AV）主干分割的新型全自动框架。该方法将先前提出的基于形状的B样条明确活动表面的半自动分割策略与两种新算法相结合，以自动量化相关的AV测量值。第一种算法将快速旋转不变的3-D广义霍夫变换与血管状暗管检测器相结合来初始化分割。在分割AV壁之后，第二种算法专注于将该表面与参考表面对齐，以估计用于进行测量的短轴（SAx）平面（在左心室流出道、瓣环、主动脉窦和窦管交界处）。该框架已在20个具有狭窄和非狭窄AV的3-D-TEE数据集中进行了测试。初始化算法对于AV轴端点的中值误差约为3毫米，总体可行性为90%。反过来，SAx检测算法显示出高度可重复性，与专家定义平面之间的变异性相比，结果难以区分。在四个层面上自动提取的测量值与专家的测量值显示出良好的一致性，一致性界限与观察者间的变异性相似。此外，另外20个狭窄AV数据集的验证集证实了该方法的适用性和准确性。所提出的方法减轻了与手动量化相关的变异性，同时显著减少了所需的分析时间（12秒与5至10分钟），这表明其在3-D-TEE中对AV形态进行自动测量以用于经导管AV植入规划方面具有吸引力。
