Advanced Computational Framework for the Automatic Analysis of the Acetabular Morphology from the Pelvic Bone Surface for Hip Arthroplasty Applications

Abstract

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.

Local Shape Similarity and Mean-Shift Curvature for Deformable Surface Mapping of Anatomical Structures

“…This value lies in the same range of the interoperator variability in manual detection, as traditionally reported in the literature [27].…”

Automating the design of resection guides specific to patient anatomy in knee replacement surgery by enhanced 3D curvature and surface modeling of distal femur shape models

“…Secondly, the morphological variability and shape degeneracy in presence of pathologic conditions, along with 3D reconstruction noise and surface resolution, reduce the reliability of the curvature assessment worsening region partition. As an outcome, classical shape curvature descriptors, as mean and Gaussian curvatures, were shown to be insufficient for automatic 3D bone segmentation [32]. In [32,33], we proved that curvaturebased algorithms can be used to improve surface segmentation and registration by exploiting the principle of mean-shifting shape curvature.…”

“…As an outcome, classical shape curvature descriptors, as mean and Gaussian curvatures, were shown to be insufficient for automatic 3D bone segmentation [32]. In [32,33], we proved that curvaturebased algorithms can be used to improve surface segmentation and registration by exploiting the principle of mean-shifting shape curvature. Basically, this approach involves the curvature smoothing, reducing the effect of noise and increasing the separation between concavities and convexities without modifying the surface topology.…”

“…Among different techniques, surface curvature was considered useful for 3D image segmentation [24,25], surface registration [26] and automatic extraction of anatomical features [27][28][29][30][31][32]. Operatively, surface curvature is an invariant measure for shape description, independent on the position and the orientation that can be exploited for reliable detection of concavities and convexities.…”

Relationship between pelvic morphology and functional parameters in standing position for patient-specific cup planning in THA

“…The method was substantially extended and enhanced to identify the posterior superior iliac spines (PSIS), the anterior inferior iliac spines (AIIS) and the ischial spine (IS) in addition. The hip joint centers (HJC) where automatically detected using a method proposed by Cerveri et al based on the curvature of the mesh (Cerveri 2011). In a similar manner, the sacral plateau was identified including the sacral promontory as well as the center of the sacral plateau (sacral center).…”