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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA
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Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA

机译：对比增强的CTA全自动分割颈动脉和椎动脉

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摘要

We propose a method for segmenting and labeling the main head and neck vessels (common, internal, external carotid, vertebral) from a contrast enhanced computed tomography angiography (CTA) volume. First, an initial centerline of each vessel is extracted. Next, the vessels are segmented using 3D active objects initialized using the first step. Finally, the true centerline is identified by smoothly deforming it away from the segmented mask edges using a spline-snake. We focus particularly on the novel initial centerline extraction technique. It uses a locally adaptive front propagation algorithm that attempts to find the optimal path connecting the ends of the vessel, typically from the lowest image of the scan to the Circle of Willis in the brain. It uses a patient adapted anatomical model of the different vessels both to initialize and constrain this fast marching, thus eliminating the need for manual selection of seed points. The method is evaluated using data from multiple regions (USA, India, China, Israel) including a variety of scanners (10, 16, 40, 64-slice; Brilliance CT, Philips Healthcare, Cleveland, OH, USA), contrast agent dose, and image resolution. It is fully successful in over 90% of patients and only misses a single vessel in most remaining cases. We also demonstrate its robustness to metal and dental artifacts and anatomical variability. Total processing time is approximately two minutes with no user interaction, which dramatically improves the workflow over existing clinical software. It also reduces patient dose exposure by obviating the need to acquire an unenhanced scan for bone suppression as this can be done by applying the segmentation masks.
机译：我们提出了一种从造影剂增强型计算机断层扫描血管造影（CTA）体积中分割和标记主要头颈部血管（常见，内部，颈外，椎体）的方法。首先，提取每个血管的初始中心线。接下来，使用第一步进行初始化的3D活动对象对血管进行分割。最后，通过使用样条曲线平滑地使真中心线远离分段的蒙版边缘，可以识别出真中心线。我们特别关注新颖的初始中心线提取技术。它使用一种局部自适应的前端传播算法，该算法试图找到连接血管末端的最佳路径，通常是从扫描的最低图像到大脑中的威利斯圆环。它使用患者适应不同血管的解剖模型来初始化和约束此快速行进，因此无需手动选择种子点。该方法使用来自多个地区（美国，印度，中国，以色列）的数据进行评估，包括各种扫描仪（10层，16层，40层，64层；华晨CT，飞利浦医疗保健，美国俄亥俄州克利夫兰），造影剂剂量和图像分辨率。它在90％以上的患者中完全成功，并且在大多数其余病例中仅遗漏了一条血管。我们还证明了其对金属和牙齿假象的稳健性以及解剖变异性。在没有用户交互的情况下，总处理时间约为2分钟，与现有临床软件相比，极大地改善了工作流程。通过消除获取未增强的骨抑制扫描的需要，它还减少了患者的剂量暴露，因为这可以通过应用分割蒙版来完成。

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