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首页> 外文会议>Conference on Image-Guided Procedures, Robotic Interventions, and Modeling >Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions
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Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions

机译：从C型臂CT数据集分割脊柱：应用于脊柱介入手术的目标区域体积

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Spinal fusion is a common procedure to stabilize the spinal column by fixating parts of the spine, In such procedures, metal screws are inserted through the patients back into a vertebra, and the screws of adjacent vertebrae are connected by metal rods to generate a fixed bridge, In these procedures, 3D image guidance for intervention planning and outcome control is required. Here, for anatomical guidance, an automated approach for vertebra segmentation from C-arm CT images of the spine is introduced and evaluated. As a prerequisite, 3D C-arm CT images are acquired covering the vertebrae of interest. An automatic model-based segmentation approach is applied to delineate the outline of the vertebrae of interest. The segmentation approach is based on 24 partial models of the cervical, thoracic and lumbar vertebrae which aggregate information about (i) the basic shape itself, (ii) trained features for image based adaptation, and (iii) potential shape variations. Since the volume data sets generated by the C-arm system are limited to a certain region of the spine the target vertebra and hence initial model position is assigned interactively. The approach was trained and tested on 21 human cadaver scans. A 3-fold cross validation to ground truth annotations yields overall mean segmentation errors of 0.5 mm for T1 to 1.1 mm for C6. The results are promising and show potential to support the clinician in pedicle screw path and rod planning to allow accurate and reproducible insertions.
机译：脊柱融合术是通过固定脊柱的部分来稳定脊柱的常见方法。在这种方法中，将金属螺钉穿过患者插入回到椎骨中，并且相邻椎骨的螺钉通过金属棒连接以产生固定的桥在这些程序中，需要用于干预计划和结果控制的3D图像指导。在这里，为进行解剖学指导，介绍了一种自动方法，用于从脊柱的C臂CT图像进行椎骨分割。作为前提，必须获取覆盖感兴趣椎骨的3D C型臂CT图像。应用基于模型的自动分割方法来描绘目标椎骨的轮廓。分割方法基于颈椎，胸椎和腰椎的24个局部模型，这些模型汇总了以下信息：（i）基本形状本身，（ii）受过训​​练的基于图像的适应性特征以及（iii）潜在的形状变化。由于C臂系统生成的体数据集仅限于脊柱的某个区域，因此目标椎骨因此被交互分配了初始模型位置。该方法已经过21次人类尸体扫描的培训和测试。对地面真相注释的3倍交叉验证产生的总体平均分割误差从T1的0.5毫米到C6的1.1毫米。结果令人鼓舞，并显示了在椎弓根螺钉路径和棒计划中支持临床医生以允许准确和可重复插入的潜力。

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