Automated Method for Computing the Morphological and Clinical Parameters of the Proximal Femur Using Heuristic Modeling Techniques

Abstract

Bone morphology and morphometric measurements of the lower limb provide significant and useful information for computer-assisted orthopedic surgery planning and intervention, surgical follow-up evaluation, and personalized prosthesis design. Femoral head radius and center, neck axis and size, femoral offset and shaft axis are morphological and functional parameters of the proximal femur utilized both in diagnosis and therapy. Obtaining this information from image data without any operator supervision or manual editing remains a practical objective to avoid variability intrinsic in the manual analysis. In this article, we propose a heuristic method that automatically computes the proximal femur morphological parameters by processing the mesh surface of the femur. The surface data are sequentially processed using geometrical properties such as symmetries, asymmetries, and principal elongation directions. Numerical methods identify the axis of the shaft of femur (least squares cylinder fitting), the head surface and center (least squares sphere fitting), and the femur neck axis and radius (minimal area of the cross section by evolutionary optimization). The repeatability of the method was tested upon 20 femur (10 left + 10 right) surfaces reconstructed from CT scans taken on cadavers. The repeatability error of the automated computation of anatomical landmarks, angles, sizes, and axes was less than 1.5 mm, 2.5 degrees, 1.0 mm, and 3.5 mm, respectively. The computed parameters were in good agreement (landmark difference: <2.0 mm; angle difference: <2.0 degrees; axes difference: <2.5 degrees; size difference: <1.5 mm) with the corresponding reference parameters manually identified in the original CT images by medical experts. In conclusion, the proposed method can improve the degree of automation of model-based hip replacement surgical systems.

Mean-shifted surface curvature algorithm for automatic bone shape segmentation in orthopedic surgery planning: a sensitivity analysis

“…Statistical models of bone structures have been investigated extensively with the aim of enhancing automation in CT and MR image segmentation [6][7][8][9][10][11][12] and reconstructing patientspecific shape models from a small number of X-ray images or even from a single image [13][14][15]. Innovative methods for bone shape analysis have been applied to 3D mesh segmentation and clinical feature detection [16][17][18][19][20][21]. In this domain, automatic shape analysis is strongly advocated because it offers the opportunity to detect morphological and clinical landmarks (MCL) with superior repeatability in comparison to human operators.…”

Biomedical Imaging and Computational Modeling in Biomechanics

“…Therefore, intra-and interobserver variability is an important factor that should be taken into account when using landmark-based clinical parameters. Several studies have been performed to assess the reproducibility of landmark identification on CT or MR (-based) images and the corresponding morphological parameters (Cerveri et al 2010, Lerner et al 2003, Nofrini et al 2004, Subburaj et al 2009, Victor et al 2009, Yoshino et al 2001), see Table 1. A direct comparison between studies is not feasible, because of the different methods that are used, but their results indicate that mean variabilities of 2-3 mm/° are not uncommon for some of the landmarks and parameters.…”

Determination of the Whiteside line on femur surface models by fitting high-order polynomial functions to cross-section profiles of the intercondylar fossa

“…A method based on geometric-based analysis allowed the extraction of the femoral head and neck from the proximal femur, and a spherefitting algorithm was used to compute the center and radius of the femoral head [13]. The automatic processing of the distal femur surface allowed 3D segmentation of the condyles and intercondylar fossa [14].…”

“…In earlier publications [13,14], we proposed a computational framework to process the 3D shape model of the femur and extract the shaft, distal and proximal parts. A method based on geometric-based analysis allowed the extraction of the femoral head and neck from the proximal femur, and a spherefitting algorithm was used to compute the center and radius of the femoral head [13].…”

“…These methods allow determination of the optimal implant size and positioning according to the computed clinical landmarks, visualization of the virtual bone resections, and simulation of the entire intervention prior to surgery [6][7][8][9][10][11][12][13][14][15][16]. As an added value, they provide functionalities, based on bone surface analysis, for designing and modeling personalized resection guides that can be used during the TKR procedure, substituting for the traditional jigs and avoiding the need for any other alignment instrument or navigation support [17][18][19].…”