Gaussian Process Diffeomorphic Statistical Shape Modelling Outperforms Angle-Based Methods for Assessment of Hip Dysplasia
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dynamic multi feature-class Gaussian process models
In model-based medical image analysis, three features of interest are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical property. Often, these are mode…
Decision MakingManagementMedical Image AnalysisDiffeomorphic brain shape modelling using Gauss-Newton optimisation
Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis. Due…
Dimensionality ReductionDiffeomorphic Spatial Transformer Networks
In this paper we propose a spatial transformer network where the spatial transformations are limited to the group of diffeomorphisms. Diffeomorphic transformations are a kind of homeomorphism, which by definition preserv…
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
Diffeomorphic registration frameworks such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) are used in computer graphics and the medical domain for atlas building, statistical latent modeling, and pairwise and …
Computational EfficiencyDimensionality ReductionProbabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unifi…
Gaussian Processesregression