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Identification of landmarks on lower limb joint from CT images for kinematics studies: a totally semi-automatic procedure

机译:用于运动学研究的CT图像识别下肢关节的地标:完全半自动程序

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The identification of an accurate, reliable and patient specific coordinate system for a bone is fundamental to analyzing the kinematics of a human joint. The accuracy in the localization of anatomical landmarks of joint surfaces is extremely important because even a small variation in their positions could induce a high variation in the definition of anatomical axes and further on the kinematics output. The aim of this study was to develop and validate a semiautomatic, accurate, and reproducible routine able to identify the position of anatomical landmarks on joint surfaces. This routine, starting from a CT of a femoral bone, used as input, is able to identify semi-automatically the femoral head and the medial and lateral distal femoral condyles. Moreover, it allows the identification of the following anatomical landmarks: the Femoral Hip Center (FHC), the Femoral Medial Epicondyle (FME) and the Femoral Lateral Epicondyle (FLE). From these points a standard coordinate system of the femur is univocally determined according to previous literature. Compared to other commercial processes, extensively used in this field, one peculiarity of this routine is that it is not necessary to generate a 3D model of the joint in order to define the anatomical landmarks. Usually, to generate a 3D lower limb model, with the commercial process, 4 to 5 hours are needed, with this approach we can significantly reduce this time. To validate the routine we analyzed ten different CTs of lower limbs. Two different tests were performed. The first test was performed to verify and check the output geometry of the model; the second test was aimed at estimating the repeatability and reproducibility of the procedure. For such a task five different operators identified for each model the three anatomical landmarks, three times each. The Intra-Class Correlation coefficient (ICC) values (intra and inter) obtained for the landmarks were always higher than 0.996. Comparing the results obtained with this routine with the results obtained using largely used commercial software we found a significant reduction of the error as regards the evaluation of landmarks in terms of inter and intra-observer variability. For example, in the worst condition, on the identification of the femoral lateral condyle point (FLE), the same operator found an average and maximum distance between the real point and the landmark found of respectively 3.5 and 8.8 mm with the use of the commercial software and of respectively 0.8 and 0.9 mm with the use of our routine.
机译:确定骨骼的准确,可靠和患者特定的坐标系是分析人体关节运动学的基础。关节表面的解剖界标定位的准确性非常重要,因为即使位置的微小变化也会导致解剖轴的定义以及运动学输出的高度变化。这项研究的目的是开发和验证一种半自动,准确且可重现的例程,该例程能够识别关节表面上解剖学界标的位置。该程序从股骨的CT开始,用作输入,能够半自动地识别股骨头以及股骨远端和内侧和外侧。此外,它还可以识别以下解剖标志:股骨髋中心(FHC),股内侧上Epi(FME)和股外侧上con(FLE)。从这些观点出发,根据先前的文献明确地确定了股骨的标准坐标系。与该领域中广泛使用的其他商业流程相比,此例程的一个独特之处在于,不必为了定义解剖学界标而生成关节的3D模型。通常,要通过商业流程生成3D下肢模型,需要4到5个小时,采用这种方法,我们可以大大减少这一时间。为了验证常规,我们分析了十种不同的下肢CT。进行了两个不同的测试。进行了第一个测试,以验证和检查模型的输出几何形状;第二项测试旨在评估该过程的可重复性和可重复性。对于此类任务,五个不同的操作员为每个模型确定三个解剖学界标,每个三个。为界标获取的类内相关系数(ICC)值(内部和内部)始终高于0.996。将使用该例程获得的结果与使用大量使用的商业软件获得的结果进行比较,我们发现,就观察者之间和观察者之间的变异性而言,关于界标评估的误差显着降低。例如,在最坏的情况下,在识别股骨外侧dy点(FLE)时,同一操作者使用商业广告发现实点与界标之间的平均和最大距离分别为3.5和8.8 mm使用我们的常规软件分别为0.8和0.9毫米。

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