Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

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“…To quantify the accuracy of our shape model, we calculated the following metrics, as suggested by Ref. 26 .…”

Section: Methodsmentioning

confidence: 99%

Morphological variation in paediatric lower limb bones

Carman

Besier

Choisne

2022

Sci Rep

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“…One application for SSAMs is generative models where new specimens are sampled based on a training population. One sub-type of this class of application is 2D-to-3D reconstruction where the 3D shape and often internal architecture of a bone is reconstructed based on one or a few 2D radiographs or dual-energy X-ray absorptiometry (DXA) images (see Reyneke et al [60] for a dedicated methodological review). In these approaches, the 3D SSAM is sampled and digitally reconstructed radiographs (DRR); i.e., projections of the SSAM are generated.…”

Section: Shape Reconstructionmentioning

confidence: 99%

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Grassi

Väänänen

Isaksson

2021

Curr Osteoporos Rep

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Purpose of Review Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation. Recent Findings Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. Summary All the reviewed application areas made considerable steps forward in the past 5–6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

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“…It maybe of interest to compute 2D objects from our 3D models in order to for example, analyse 2D images such as X-ray [29], [30]. This can be achieved in our modelling approach through a transformation that maps a 3D model instance into the 2D image space.…”

Section: D Projection From 3d Samplesmentioning

confidence: 99%

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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Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework

Cited by 54 publications

References 116 publications

Morphological variation in paediatric lower limb bones

Morphological variation in paediatric lower limb bones

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Dynamic Multi-object Gaussian Process Models

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