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Geometric Transformation Algorithm for Acetabular Cup Orientation: Converting 2D Radiographic Projections to 3D
Gökmen Aktas1, Lukas Hensler2, Sabine Dippel2
1Department of Trauma Surgery, Hannover Medical School, Hannover, Germany; aktas.goekmen@mh-hannover.de.
Background/Aim:
Hip replacement is one of the most common and successful surgeries of our time. However, with increasing numbers of total hip arthroplasties, postoperative complications due to inadequate cup positioning are rising. Accurate measurement of acetabular cup inclination and anteversion is critical for optimal outcomes. While various measurement systems exist, they often require additional hardware, specialized training, or lack integration across the care continuum. This study aims to develop and validate software for automated measurement of acetabular cup positioning, comparing its performance to standard CAD measurements.
Materials And Methods:
The geometric basis for analyzing the acetabular cup is that a hemisphere in 2D projection forms an ellipse, described by a conic section equation. We developed a Python-based software utilizing computer vision techniques for edge detection and ellipse fitting to determine anteversion and inclination from standard radiographs. The software was trained and validated using X-ray images of a pelvic phantom with a conventional acetabular cup (Allofit, Zimmer Biomet) at various predefined angles. CAD software (MediCAD) served as the validation standard. The intraclass correlation coefficient (ICC) quantified agreement between methods.
Results:
A total of 140 AP X-ray images were analyzed. Inclination averaged 50.69°±15.86° with our software versus 49.40°±15.24° with CAD. Anteversion averaged 14.36°±9.08° versus 14.75°±8.88° with CAD. The ICC was 0.994 for inclination and 0.992 for anteversion (both p<0.001), demonstrating excellent agreement.
Conclusion:
Our computer-assisted measurement technique demonstrates excellent concordance with standard methodologies while offering workflow advantages. These results provide a foundation for implementation within computer vision models for automated spatial positioning recognition. Future development will focus on enhancing automation, validating across diverse populations and implant designs, and comparing with 3D computed tomography (CT) measurements.
