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.

This article analyzes a realistic kinematic model of the trapezio-metacarpal (TM) joint in the human thumb that involves two non-orthogonal and non-intersecting rotation axes. The estimation of the model parameters, i.e. the position and orientation of the two axes with respect to an anatomical coordinate system, was carried out by processing the motion of nine retroreflective markers, externally attached to the hand surface, surveyed by a video motion capture system. In order to compute the model parameters, prototypical circumduction movements were processed within an evolutionary optimization approach. Quality and reproducibility in assessing the parameters were demonstrated across multiple testing sessions on 10 healthy subjects (both left and right thumbs), involving the complete removal of all markers and then retesting. Maximum errors of less than 5 mm in the axis position and less than 6 degrees in the orientation were found, respectively. The inter-subject mean distance between the two axes was 4.16 and 4.71 mm for right and left TM joints, respectively. The inter-subject mean relative orientation between the two axes was about 106 and 113 degrees for right and left TM joints, respectively. Generalization properties of the model were evaluated quantitatively on opposition movements in terms of distance between measured and predicted marker positions (maximum error less than 5 mm). The performance of the proposed model compared favorably with the one (maximum error in the range of 7-8 mm) obtained by applying a universal joint model (orthogonal and intersecting axes). The ability of in vivo estimating the parameters of the proposed kinematic model represents a significant improvement for the biomechanical analysis of the hand motion.

In this paper, we propose a method to estimate the parameters of a double hinge model of the trapeziometacarpal joint (TMC) by MRI-based motion analysis. The model includes two non-orthogonal and non-intersecting rotation axes accounting for flexion-extension (F-E) and adduction-abduction (A-A). We evaluated the quality of the estimated model parameters in the prediction of the relative motion of the first metacarpal bone with respect to the trapezium. As a result, we obtained that: (a) the estimated location and orientation of the F-E and A-A axes were in agreement with previous in vitro studies, (b) the motion of the first metacarpal predicted by the 2 degrees of freedom (2DoF) model exhibits a maximum surface distance error in the range of about 2 mm and (c) four thumb postures at the boundary of the TMC range of motion are sufficient to provide a good estimation of the 2DoF TMC kinematic model and good reproducibility (~1.7 mm) of the real thumb motion at TMC level.

Innovative methods for morphological and functional analysis of bones have become a primary objective in the development of planning systems for total knee replacement (TKR). These methods involve the interactive identification of clinical landmarks (reference points, distances, angles, and functional axes of movement) and the determination of the optimal implant size and positioning. Among the functional axes used to estimate the correct alignment of the femoral component, the Whiteside line, namely, the anterior-posterior (AP) direction, is one of the most common. In this paper, we present a computational framework that allows automatic identification of the Whiteside line.The approach is based on geometric analysis of the saddle shape of the intercondylar fossa to extract the principal line in the AP direction. A plane parallel to the frontal plane is moved in the AP direction to obtain the 2D profiles of the intercondylar fossa. Each profile is fitted to a fifth-order polynomial curve and its maximum curvature point computed. The point set collected across all the profiles is then processed to compute the principal direction. The 2D profile-fitting and 3D line-fitting residual errors were analyzed to study the relationship between the intercondylar fossa aspect and the nominal saddle surface. The method was validated using femur specimens from elderly subjects reconstructed from CT scans. The repeatability of the method was evaluated across five different femur surface resolutions.For comparison, three expert orthopaedic surgeons identified, by virtual palpation, the Whiteside line on the same 3D femur models. The repeatability (median angular error) of the Whiteside lines computed by the automated method and by manual virtual palpation, was approximately 1.0 and 3.5 , respectively. The angular skew error between the two axes, measured on the axial plane, averaged approximately 4.00 (SD: 2.64 ) with no statistical difference. The automated method therefore proved more reproducible and was in agreement with the manual method. We conclude that operatorindependent methods, such the one presented in this paper, can be favorably introduced into orthopaedic surgical planning systems.

2D- and 3D-based innovative methods for surgical planning and simulation systems in orthopedic surgery have emerged enabling the interactive or semi-automatic identification of the clinical landmarks (CL) on the patient individual virtual bone anatomy. They enable the determination of the optimal implant sizes and positioning according to the computed CL, the visualization of the virtual bone resections and the simulation of the overall intervention prior to surgery. The virtual palpation of CL, highly dependent upon the examiner's expertise, was proved to be time consuming and to suffer from considerable inter-observer variability. In this article, we propose a fully automatic algorithmic framework that processes the pelvic bone surface, integrating surface curvature analysis, quadric fitting, recursive clustering and clinical knowledge, aiming at computing the main parameters of the acetabulum. The performance of the method was evaluated using pelvic bone surfaces reconstructed from CT scans of cadavers and subjects with pathological conditions at the hip joint. The repeatability error of the automated computation of acetabular center, size and axis parameters was less than 1 mm, 0.5 mm, and 1.5°, respectively. The computed parameters were in agreement (<1.5 mm; <0.5 mm; <3.0°) with the corresponding reference parameters manually identified in the original datasets by medical experts. According to our results, the proposed method is put forward to improve the degree of automation of image/model-based planning systems for hip surgery.