Zheng Yefeng, John Matthias, Liao Rui, Boese Jan, Kirschstein Uwe, Georgescu Bogdan, Zhou S Kevin, Kempfert Jörg, Walther Thomas, Brockmann Gernot, Comaniciu Dorin
Siemens Corporate Research, Princeton, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):476-83. doi: 10.1007/978-3-642-15705-9_58.
C-arm CT is an emerging imaging technique in transcatheter aortic valve implantation (TAVI) surgery. Automatic aorta segmentation and valve landmark detection in a C-arm CT volume has important applications in TAVI by providing valuable 3D measurements for surgery planning. Overlaying 3D segmentation onto 2D real time fluoroscopic images also provides critical visual guidance during the surgery. In this paper, we present a part-based aorta segmentation approach, which can handle aorta structure variation in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three aortic hinge points, three commissure points, and two coronary ostia) are also detected automatically in our system. Under the guidance of the detected landmarks, the physicians can deploy the prosthetic valve properly. Our approach is robust under variations of contrast agent. Taking about 1.4 seconds to process one volume, it is also computationally efficient.
C形臂CT是经导管主动脉瓣植入术（TAVI）手术中一种新兴的成像技术。在C形臂CT容积中进行自动主动脉分割和瓣膜标志点检测，通过为手术规划提供有价值的三维测量，在TAVI中具有重要应用。将三维分割叠加到二维实时荧光透视图像上，也能在手术过程中提供关键的视觉引导。在本文中，我们提出了一种基于部分的主动脉分割方法，该方法能够在容积中缺少主动脉弓和降主动脉的情况下处理主动脉结构变化。整个主动脉模型被分为四个部分：主动脉根部、升主动脉、主动脉弓和降主动脉。应用判别学习分别为每个部分训练一个检测器，以利用专家标注数据集中嵌入的丰富领域知识。我们的系统还能自动检测八个重要的主动脉瓣标志点（三个主动脉铰链点、三个连合点和两个冠状动脉口）。在检测到的标志点的引导下，医生能够正确地植入人工瓣膜。我们的方法在造影剂变化的情况下具有鲁棒性。处理一个容积大约需要1.4秒，计算效率也很高。
