Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints

• URL: http://arxiv.org/abs/2001.07904v1
• Date: Wed, 22 Jan 2020 07:57:36 GMT
• Title: Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints
• Authors: Jean-Rassaire Fouefack, Bhushan Borotikar, Tania S. Douglas, Val\'erie Burdin and Tinashe E.M. Mutsvangwa
• Abstract summary: A principled and robust way to combine shape and pose features has been illusive due to three main issues. We propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints. The framework affords an efficient generative dynamic multi-object modelling platform for biological joints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Statistical shape models (SSMs) are state-of-the-art medical image analysis tools for extracting and explaining features across a set of biological structures. However, a principled and robust way to combine shape and pose features has been illusive due to three main issues: 1) Non-homogeneity of the data (data with linear and non-linear natural variation across features), 2) non-optimal representation of the $3D$ motion (rigid transformation representations that are not proportional to the kinetic energy that move an object from one position to the other), and 3) artificial discretization of the models. In this paper, we propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints in a continuous domain. Specifically, we propose to normalise shape and dynamic spatial features in the same linearized statistical space permitting the use of linear statistics; we adopt an optimal 3D motion representation for more accurate rigid transformation comparisons; and we provide a 3D shape and pose prediction protocol using a Markov chain Monte Carlo sampling-based fitting. The framework affords an efficient generative dynamic multi-object modelling platform for biological joints. We validate the framework using a controlled synthetic data. Finally, the framework is applied to an analysis of the human shoulder joint to compare its performance with standard SSM approaches in prediction of shape while adding the advantage of determining relative pose between bones in a complex. Excellent validity is observed and the shoulder joint shape-pose prediction results suggest that the novel framework may have utility for a range of medical image analysis applications. Furthermore, the framework is generic and can be extended to n$>$2 objects, making it suitable for clinical and diagnostic methods for the management of joint disorders.

Related papers

• An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation [45.820901263103806]
  Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs) We develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space. We extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability.
  arXiv  Detail & Related papers  (2024-03-10T21:33:53Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• Sparse Graphical Linear Dynamical Systems [1.6635799895254402]
  Time-series datasets are central in machine learning with applications in numerous fields of science and engineering. This work proposes a novel approach to bridge the gap by introducing a joint graphical modeling framework. We present DGLASSO, a new inference method within this framework that implements an efficient block alternating majorization-minimization algorithm.
  arXiv  Detail & Related papers  (2023-07-06T14:10:02Z)
• Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes [3.469001874498102]
  thesis focuses on data that has complex-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and interpretable manner. practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data.
  arXiv  Detail & Related papers  (2023-01-15T05:39:30Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Dynamic multi feature-class Gaussian process models [0.0]
  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.
  arXiv  Detail & Related papers  (2021-12-08T15:12:47Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion [15.359134407309726]
  Our model decomposes highly correlated skeleton data into a set of few spatial basis of switching temporal processes. This results in a dynamical deep generative latent model that parses the meaningful intrinsic states in the dynamics of 3D pose data.
  arXiv  Detail & Related papers  (2021-06-18T23:58:49Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.