Prediction of femoral head coverage from articulated statistical shape models of patients with developmental dysplasia of the hip

Abstract

Developmental dysplasia of the hip (DDH) is commonly described as reduced femoral head coverage due to anterolateral acetabular deficiency. Although reduced coverage is the defining trait of DDH, more subtle and localized anatomic features of the joint are also thought to contribute to symptom development and degeneration.These features are challenging to identify using conventional approaches. Herein, we assessed the morphology of the full femur and hemi-pelvis using an articulated statistical shape model (SSM). The model determined the morphological and posebased variations associated with DDH in a population of Japanese females and established which of these variations predict coverage. Computed tomography (CT) images of 83 hips from 47 patients were segmented for input into a correspondence-based SSM. The dominant modes of variation in the model initially represented scale and pose. After removal of these factors through individual bone alignment, femoral version and neck-shaft angle, pelvic curvature, and acetabular version dominated the observed variation. Femoral head oblateness and prominence of the acetabular rim and various muscle attachment sites of the femur and hemipelvis were found to predict 3D CT-based coverage measurements (R 2 = 0.5-0.7 for the full bones, R 2 = 0.9 for the joint). Statement of Clinical Significance: Currently, clinical measurements of DDH only consider the morphology of the acetabulum. However, the results of this study demonstrated that variability in femoral head shape and several muscle attachment sites were predictive of femoral head coverage. These morphological differences may provide insight into improved clinical diagnosis and surgical planning based on functional adaptations of patients with DDH.

Statistical multi-level shape models for scalable modeling of multi-organ anatomies

“…Particle-based shape modeling (PSM) Cates et al (2007) and Cates et al (2017a) , in particular, is a state-of-the-art computational approach for constructing point distribution models (PDM) via automatically placing a dense set of corresponding landmarks on a set of shapes. The scientific and clinical utility of PSM have been demonstrated in image and shape analysis [e.g., Bhalodia et al (2021) and Shigwan et al (2020) ], neuroscience [e.g., Sultana et al (2019) and Audette et al (2017) ], biological phenotyping [e.g., Jones et al (2013) and Cates et al (2017b) ], cardiology [e.g., Bieging et al (2018) and Goparaju et al (2022) ], and orthopaedics [e.g., Lenz et al (2021) , Goparaju et al (2022) , Krähenbühl et al (2020) , Jacxsens et al (2020) , Atkins et al (2017a) , Atkins et al (2019) , Atkins et al (2017b) , and Atkins et al (2022) ].…”

Prediction of 3-Dimensional Coverage Surface Area of the Femoral Head in Hip Dysplasia Through Conventional Computed Tomography

“… 13 , 17 , 30 Several investigators have assessed 3D femoral head coverage beyond 2D evaluation. 2 , 5 , 17 , 22 , 27 , 30 Nepple et al 22 measured radial coverage angles at different acetabular clockface positions in 3D computed tomography (CT) and showed considerable variability in deficiency patterns, including global and locally reduced coverage in the anterosuperior and posterosuperior regions, providing an in-depth characterization of the femoral head coverage. 22 , 32 Thus, 3D femoral head coverage assessment is clinically important for pre- and postoperative evaluation of hip dysplasia.…”