Spinal deformity is a disease that causes a three-dimensional deformation of the spinal column. When it worsens, surgery is required to screw correction rods to the spinal column. However, the surgery requires intraoperative rod bending work, which burdens the patients and causes unexpected rod breakage inside the body. Therefore, "pre-bent" rods comprising several rods with standardized shapes have been proposed to solve these problems. When designing pre-bent rods, knowing the number of rods to be prepared and the kinds of shapes required is essential. In this paper, we propose a geometric processing technique to identify an optimal set of these standardized pre-bent rod shapes for surgeries on adult spinal deformity and describe the similarity evaluation among existing rod shapes using CT scan, medial axis extraction, and iterative closest point algorithm. Moreover, we present the derivation of standardized rod shapes using hierarchical cluster analysis and the best fit of the B-spline curve to each cluster. Finally, we discuss the effectiveness of prebent rod shapes derived from CT scans of 26 existing rods of 13 patients.

The most common way to retrieve symmetry information (i.e., the planes and axes of symmetry) in 3D CAD models is through visual recognition by engineers. However, engineers are not able to visually recognize exact symmetry in any CAD model, and their ability to recognize symmetry decreases as the number of CAD models increases. To overcome these limitations, computer-aided symmetry detection is employed, which enables the (semi)automatic extraction of the symmetry information in CAD models. Hence, the present paper introduces a symmetry detection framework for 3D CAD models with boundary representation. The novelty of this research was that it addressed the detection of exact and partial axi- and reflectional symmetry in CAD models with analytic and numeric surfaces. Further, symmetry measures were proposed to differentiate exact, partial, or non-symmetry in the CAD model. The framework was implemented into a state-of-the-art CAD system and subjected to performance and time complexity validation. The results showed that the implemented framework’s performance was 0.94 F1-score, and the time complexity was linear with respect to the number of faces in the CAD model. Hence, it was concluded that the framework is suitable for industrial applications to support engineers in symmetry detection.

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