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首页> 外文会议>Conference on medical imaging >Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis
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Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis

机译：使用定向扩张和连接分析的多相肝CT中肝动脉的分割

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Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0.55 ± 0.27 and 12.7 ± 7.9 mm (mean standard deviation), respectively.
机译：多相计算断层扫描（CT）图像中肝动脉的分割是肝脏手术规划中必不可少的。在图像采集期间，通过注射造影剂来增强肝动脉。由于非最佳对比度定时，通常不会稳定获取增强的信号。其他血管结构，例如肝静脉或门静脉，也可以在动脉阶段提高，这可能对分段结果产生不利影响。此外，由于其小直径，动脉可能患有部分体积效应。为了克服这些困难，我们提出了一种稳健的肝动脉分段框架，需要最小的用户交互。首先，在动脉相CT图像上施加有效的多尺寸Hessian基血管滤镜，旨在增强具有指定直径范围的血管结构。其次，使用贝叶斯分类器处理血管响应以识别最可能的血管结构。考虑到血管滤波器通常不理想地在血管分叉上或通过噪声损坏的区段，提出了两个血管重新连接技术。第一技术使用定向形态操作员沿着其中心线方向扩展血管区段，试图填充破碎的血管段之间的间隙。第二种技术分析了血管段的连接性，并重新连接断开的段和分支。最后，重建3D血管树。使用肝脏的18个CT图像评估了该算法。为了定量测量分段和参考容器树之间的相似性，计算骨架覆盖和平均对称距离以量化参考和分段血管骨架之间的一致性，其平均为0.55±0.27和12.7±7.9 mm（平均标准偏差） ， 分别。

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