Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints

Agrawal, Praful; Mozingo, Joseph D.; Elhabian, Shireen; Anderson, Andrew E.; Whitaker, Ross T.

doi:10.1007/978-3-030-61056-2_9

Cited by 5 publications

(11 citation statements)

References 26 publications

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“…Our findings suggest that indeed, a unified multi-object modelling approach is much better suited for such segmentation scenarios. One benefit of the DMFC-GPM is the approach maintains the statistics of single object variation such as SSMs [4], [7], SAMs [3], [6] or SSPMs [12], [13], [14], [15] and these can be retrieved on demand without any need to retrain the model. An additional benefit is that where strong correlations between different objects exist, these can be leveraged to make a prediction of a targeted object with partial or no observation of the target in the image.…”

Section: Discussionmentioning

confidence: 99%

“…This allows the integration of inter-object shape correlations in the latent space, which is important for understanding anatomicalphysiological relationships between articulated objects. Statistical modelling of shape and pose: Similar to the shape case, pose variation analysis using linear descriptors has been previously reported [12], [13], [14], [15]. Reports abound in the literature on efforts to use methods more suited for managing the non-linearity of pose descriptors [5], [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Multi-object Gaussian Process Models

Fouefack¹,

Borotikar²,

Douglas³

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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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 modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing multiple objects in an image using multiple single object models may lead to large errors and uncertainties, especially around organ boundaries. A question that comes to mind is what kind of advantages can be gained from combining the three features of interest in the same statistical space for analysing images. This study presents a statistical modelling method for automatic learning of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). A DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variation. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose feature variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape feature prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications in a multitude of scenarios including the management of musculoskeletal disorders, clinical decision making and image processing.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic Multi-object Gaussian Process Models

Fouefack¹,

Borotikar²,

Douglas³

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

View full text Add to dashboard Cite

show abstract

“…From this initial Unscaled SSM , a two‐step alignment method described by Agrawal and colleagues was used to remove variability in size and pose from the model (Figure 1). 33 In the first alignment step, hip size (i.e., scale) was normalized across the population via generalized Procrustes analysis, resulting in the SSM with Pose . In the second step, mean femur and hemi‐pelvis shapes generated from the SSM with Pose were used as alignment templates to remove individual pose variations of the separate bones (femur and hemi‐pelvis); these shapes were then used to generate the SSM of Shape (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…In the second step, mean femur and hemi‐pelvis shapes generated from the SSM with Pose were used as alignment templates to remove individual pose variations of the separate bones (femur and hemi‐pelvis); these shapes were then used to generate the SSM of Shape (Figure 1). 33 Principal component analysis (PCA) was used to reduce the dimensionality of the correspondence model into a smaller number of modes that described dominant shape variations. PCA was applied to the correspondence model of the Unscaled SSM and after each step of the alignment process.…”

Section: Methodsmentioning

confidence: 99%

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

Atkins

Agrawal

Mozingo

et al. 2021

Journal Orthopaedic Research

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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.

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Morphology of the scaphotrapeziotrapezoid joint: A multi‐domain statistical shape modeling approach

Trentadue,

Thoreson,

Lopez

et al. 2024

Journal Orthopaedic Research

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The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three‐dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi‐domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM‐generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three‐dimensional triangular surface meshes. A multi‐domain, particle‐based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface‐normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities—a proxy for joint space—were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging‐derived STT joint space.

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Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints

Cited by 5 publications

References 26 publications

Dynamic Multi-object Gaussian Process Models

Dynamic Multi-object Gaussian Process Models

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

Morphology of the scaphotrapeziotrapezoid joint: A multi‐domain statistical shape modeling approach

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