Waechter I, Kneser R, Korosoglou G, Peters J, Bakker N H, van der Boomen R, Weese J
Philips Research Aachen, Germany.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):526-33. doi: 10.1007/978-3-642-15705-9_64.
Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In addition, we present a new method for the detection of the coronary ostia that exploits the model-based segmentation and show, how a number of clinical measurements such as diameters and the distances between aortic valve plane and coronary ostia can be derived that are important for procedure planning. Validation results are based on accurate reference annotations of 20 CT images from different patients and leave-one-out tests. They show that model adaptation can be done with a mean surface-to-surface error of 0.5mm. For coronary ostia detection a success rate of 97.5% is achieved. Depending on the measured quantity, the segmentation translates into a root-mean-square error between 0.4 - 1.2mm when comparing clinical measurements derived from automatic segmentation and from reference annotations.
最近，已开发出用于微创主动脉瓣植入的新技术，这就需要能够评估瓣膜解剖结构的规划工具以及支持在X射线引导下进行植入的引导工具。从CT图像中提取主动脉瓣解剖结构对于此类工具至关重要，为此我们提出了一种基于模型的方法。此外，我们提出了一种检测冠状动脉口的新方法，该方法利用基于模型的分割，并展示了如何得出一些对手术规划很重要的临床测量值，如直径以及主动脉瓣平面与冠状动脉口之间的距离。验证结果基于对来自不同患者的20幅CT图像的准确参考标注以及留一法测试。结果表明，模型适配的平均表面到表面误差可达0.5毫米。对于冠状动脉口检测，成功率达到了97.5%。根据测量的量，在比较自动分割得出的临床测量值和参考标注得出的临床测量值时，分割转化为均方根误差在0.4 - 1.2毫米之间。
