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Dynamic multi feature-class Gaussian process models.

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Title: Dynamic multi feature-class Gaussian process models.
Authors: Fouefack, Jean-Rassaire1,2,3 (AUTHOR) fjrassaire@gmail.com, Borotikar, Bhushan1,4 (AUTHOR) bhushan.borotikar@scmia.edu.in, Lüthi, Marcel5 (AUTHOR) marcel.luethi@unibas.ch, Douglas, Tania S.2,3 (AUTHOR), Burdin, Valérie1,3 (AUTHOR) valerie.burdin@imt-atlantique.fr, Mutsvangwa, Tinashe E.M.2,3 (AUTHOR) tinashe.mutsvangwa@uct.ac.za
Source: Medical Image Analysis. Apr2023, Vol. 85, pN.PAG-N.PAG. 1p.
Subjects: Gaussian processes, Image analysis, Principal components analysis, Joints (Anatomy), Image processing, Markov chain Monte Carlo, Latent variables
Abstract: In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features 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 articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries. Questions that come to mind are the feasibility of building a unique model that combines all three features of interest in the same statistical space, and what advantages can be gained for image analysis. This study presents a statistical modelling method for automatic analysis of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). The DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variations. 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 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 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 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. • A principled way to represent shape, pose and intensity feature classes. • A unified latent space for modelling of multiple shapes, poses and intensities. • A Gaussian process-based modelling paradigm for musculoskeletal disorder management. • A method for human joint segmentation and 3D reconstruction from medical images. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features 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 articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries. Questions that come to mind are the feasibility of building a unique model that combines all three features of interest in the same statistical space, and what advantages can be gained for image analysis. This study presents a statistical modelling method for automatic analysis of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). The DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variations. 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 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 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 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. • A principled way to represent shape, pose and intensity feature classes. • A unified latent space for modelling of multiple shapes, poses and intensities. • A Gaussian process-based modelling paradigm for musculoskeletal disorder management. • A method for human joint segmentation and 3D reconstruction from medical images. [ABSTRACT FROM AUTHOR]
ISSN:13618415
DOI:10.1016/j.media.2022.102730