Dynamic multi feature-class Gaussian process models
- URL: http://arxiv.org/abs/2112.04495v1
- Date: Wed, 8 Dec 2021 15:12:47 GMT
- Title: Dynamic multi feature-class Gaussian process models
- Authors: Jean-Rassaire Fouefack, Bhushan Borotikar, Marcel L\"uthi, Tania S.
Douglas, Val\'erie Burdin and Tinashe E.M. Mutsvangwa
- Abstract summary: This study presents a statistical modelling method for automatic learning of shape, pose and intensity features in medical images.
A DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variation.
The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. 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
including the management of musculoskeletal disorders and clinical decision
making
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