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首页> 外文会议>Conference on Medical Imaging: Computer-Aided Diagnosis >Quantitative Measurement of Deformational Plagiocephaly and Brachycephaly at the Point-of-care
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Quantitative Measurement of Deformational Plagiocephaly and Brachycephaly at the Point-of-care

机译：定量测量变形斑术和Brachycephaly在护理点

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Purpose: to develop and validate algorithms that enable a novice user to quantitatively measure the head shape parameters associated with deformational plagiocephaly, and brachycephaly (DPB) using a smartphone or tablet. Method: We have developed a technology (called SoftSpot™) based on advanced imaging algorithms to detect different types and severity of DPB from top-view photos of the head acquired by a novice user at the point-of-care, i.e. in pediatric offices, and at home. Currently, the head shape parameters are measured using either a 3D scanner or a mechanical caliper by a specialist such a pediatric neurosurgeon or an orthotist. In our approach, the head contour is extracted semi-automatically using the intelligent scissors method. We then automatically compute two indices used in the clinical determination of the DPB: the cranial index (CI), and the cranial vault asymmetry index (CVAI). In this paper, we also present methods to quantify, and compensate for the user variability in the acquisition of photos, including camera angle, and distance from the head, by combining the results from different camera positions. We compared the results of our technology with ground truth measurements from 53 infants with DPB, and normal cranial parameters. Accuracy analysis was performed by Bland Altman (BA) method, and the Spearman correlation test. Results: The Spearman correlation coefficients between the new 2D method, and the 3D ground truth were 0.94 (p＜0.001). and 0.96 (p＜0.001) for CI and CVAI, respectively. Different camera angles, and distances from the head resulted in variation in CI and CVAI in the range of [-2.0. 6.0]. and [-4.0, 4.0] units, respectively. The limit of agreement was reduced from [-3.6, 5.3], and [-3.6, 4.2] to [-0.5, 3.0], and [-1.3, 1.6] for CI and CVAI, respectively, by combining results from different camera angles, and positions in our method. The overall accuracy of the proposed technology for DPB detection was 100%. Conclusions: Photographic 2D images can be accurately analyzed to assess DPB at the point-of-care. By compensating for the error from variable camera angles, and distance from the head, our technology eliminates user variability. The algorithms will be packaged in a mobile application to enable the use of the technology at the point-of-care.
机译：目的：开发和验证使新手用户能够使用智能手机或平板电脑定量测量与变形屏腔相关联的头部形状参数的算法，以及使用智能手机或平板电脑的Brachycephaly（DPB）。方法：我们开发了一种基于高级成像算法的技术（称为SoftSpot™），以检测DPB的DPB的不同类型和严重性，从新手用户在护理点，即在儿科办公室，在家里。目前，使用3D扫描仪或机械卡钳通过专家这样的儿科神经外科医生或矫形器来测量头部形状参数。在我们的方法中，使用智能剪刀法自动提取头轮廓。然后，我们自动计算用于DPB的临床测定中使用的两个指数：颅指数（CI）和颅骨拱形不对称指数（CVAI）。在本文中，我们还通过将来自不同摄像机位置的结果组合的结果，提出量化的方法，并补偿包括摄像机角度的用户可变性，包括摄像机角度和距离头的距离。我们将技术的结果与DPB的53名婴儿和正常的颅骨参数进行了比较了我们技术的结果。 Bland Altman（BA）方法和Spearman相关试验执行精度分析。结果：新的2D方法与3D地面真相之间的矛盾与3D接地事实之间的相关系数为0.94（P <0.001）。 CI和CVAI分别为0.96（P <0.001）。不同的相机角度，以及距头部的距离导致CI和CVAI的变化在[-2.0的范围内。 6.0]。和[-4.0,4.0]单位。通过组合不同的相机角度的结果，分别从[-3.6,5.3]和[-3.6,4.2]和[-3.6,4.2]和[-1.3,1.2]和[-1.3,1.0]和[-1.3,1.6]的限制减少和我们方法中的位置。所提出的DPB检测技术的总体精度为100％。结论：可以准确地分析摄影2D图像以在护理点评估DPB。通过补偿可变摄像机角度的误差，以及从头的距离，我们的技术消除了用户可变性。该算法将包装在移动应用程序中，以便在护理点之前使用该技术。

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