Gaussian Process Diffeomorphic Statistical Shape Modelling Outperforms Angle-Based Methods for Assessment of Hip Dysplasia
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Dysplasia is a recognised risk factor for osteoarthritis (OA) of the hip,
early diagnosis of dysplasia is important to provide opportunities for surgical
interventions aimed at reducing the risk of hip OA. We have developed a
pipeline for semi-automated classification of dysplasia using volumetric CT
scans of patients' hips and a minimal set of clinically annotated landmarks,
combining the framework of the Gaussian Process Latent Variable Model with
diffeomorphism to create a statistical shape model, which we termed the
Gaussian Process Diffeomorphic Statistical Shape Model (GPDSSM). We used 192 CT
scans, 100 for model training and 92 for testing. The GPDSSM effectively
distinguishes dysplastic samples from controls while also highlighting regions
of the underlying surface that show dysplastic variations. As well as improving
classification accuracy compared to angle-based methods (AUC 96.2% vs 91.2%),
the GPDSSM can save time for clinicians by removing the need to manually
measure angles and interpreting 2D scans for possible markers of dysplasia.
