François Chung, Ph.D.

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LAP 2011 - Livre

LAP 2011 – Livre

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Publication

François Chung; Regional appearance modeling for model-based image segmentation: Methodological approaches to improve the accuracy of model-based image segmentation; Lambert Academic Publishing (LAP), Saarbrücken, 2011; ISBN: 978-3844322095.

Abstract

This thesis presents a novel appearance prior for model-based image segmentation. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon an Expectation–Maximization (EM) clustering of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical approaches based on Principal Component Analysis (PCA), the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex. Comparative results on liver profiles from Computed Tomography (CT) images show that MPAM outperforms PCA-based appearance models. Finally, methods for the analysis of lower limb structures from Magnetic Resonance (MR) images are presented. A first part deals with the creation of subject-specific models for kinematic simulations of the lower limbs. In a second part, the performance of statistical models is compared in the context of lower limb bone segmentation when only a small number of datasets is available for training.

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3D Anatomical Human (projet INRIA)
Ph.D. Thesis 2011 (thèse de doctorat)

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LAP – Lambert Academic Publishing

TVC 2011 - Article de revue scientifique

TVC 2011 – Article de revue scientifique

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Publication

François Chung, Jérôme Schmid, Nadia Magnenat-Thalmann, Hervé Delingette; Comparison of statistical models performance in case of segmentation using a small amount of training datasets; In: The Visual Computer (TVC), 27 (2), pp. 141-151, 2011.

Abstract

Model-based image segmentation has been extensively used in medical imaging to learn both shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using Magnetic Resonance (MR) images when only a small number of datasets is available for training. For shape, both priors based on Principal Component Analysis (PCA) and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from Expectation-Maximization (EM) clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.

Mots-clés

  • clustering
  • model-based segmentation
  • principal component analysis
  • statistical models

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Articles associés

3D Anatomical Human (projet INRIA)
Ph.D. Thesis 2011 (thèse de doctorat)

Ph.D. Thesis 2011 - Thèse de doctorat

Ph.D. Thesis 2011 – Thèse de doctorat

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Publication

François Chung; Regional appearance modeling for deformable model-based image segmentation; Thèse de doctorat (Ph.D. Thesis), Mines ParisTech, Centre de Mathématiques Appliquées, 2011.

Abstract

This thesis presents a novel appearance prior for model-based image segmentation. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon an Expectation-Maximization (EM) clustering of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical approaches based on Principal Component Analysis (PCA), the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex.

First, we explain how to build a MPAM from a training set of meshes and images. The clustering of intensity profiles and the determination of the number of appearance regions by a novel model order selection criterion are explained. A spatial regularization approach to spatially smooth the clustering of profiles is presented and the projection of the appearance information from each dataset on a reference mesh is described.

Second, we present a boosted clustering based on spectral clustering, which optimizes the clustering of profiles for segmentation purposes. The representation of the similarity between data points in the spectral space is explained. Comparative results on liver profiles from Computed Tomography (CT) images show that our approach outperforms PCA-based appearance models.

Finally, we present methods for the analysis of lower limb structures from Magnetic Resonance (MR) images. In a first part, our technique to create subject-specific models for kinematic simulations of lower limbs is described. In a second part, the performance of statistical models is compared in the context of lower limb bones segmentation when only a small number of datasets is available for training.

Mots-clés

  • appearance modeling
  • liver
  • lower limbs
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

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Articles associés

3D Anatomical Human (projet INRIA)
CVIU 2013 (article de revue scientifique)
LAP 2011 (livre)

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PASTEL
Mines ParisTech

3D Anatomical Human

3D Anatomical Human

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Projet INRIA @Genève, Suisse (2010). Le projet 3D Anatomical Human (3DAH) est un réseau européen de recherche et de formation (RTN) de type Marie Curie. L'objectif est d'accroître le développement de technologies et de connaissances autour des représentations virtuelles du corps humain pour des applications médicales. Plus précisément, le réseau a pour objectif de développer des modèles 3D réalistes et fonctionnels du système musculo-squelettique humain.

Les principaux domaines de recherche sont les suivants:

  • analyse du contrôle moteur: simulation du système locomoteur;
  • infographie: simulation efficace des êtres humains;
  • biomécanique: caractérisation tissulaire précise et simulation mécanique;
  • traitement d'images: modélisation d'organes à partir d'images;
  • orthopédie: résolution de problèmes pathologiques particuliers.

Les partenaires de ce projet sont:

  • AAU - Aalborg Universitet (DK);
  • CRS4 - Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna (IT);
  • EPFL - École Polytechnique Fédérale de Lausanne (CH);
  • INRIA Sophia-Antipolis - Institut National de Recherche en Informatique et en Automatique (FR);
  • UNIGE - Université de Genève (CH);
  • UCL - University College London (UK);
  • VUB - Vrije Universiteit Brussel (BE).

Dans ce projet, mon travail consiste à segmenter les structures anatomiques des membres inférieurs (p. ex. muscles, os et ligaments) à partir d'images par résonance magnétique (IRM) statiques et dynamiques. En raison de la variabilité de ces structures, la segmentation est effectuée en combinant un recalage non-rigide de l'image avec une segmentation par modèle déformable.

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Articles associés

TVC 2011 (article de revue scientifique)
CBM 2009 (acte de conférence)
MICCAI 2009 (acte de conférence)

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3DAH – 3D Anatomical Human
INRIA Sophia-Antipolis – Institut National de Recherche en Informatique et en Automatique

CBM 2009 - Acte de conférence

CBM 2009 – Acte de conférence

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Publication

Tobias Heimann, François Chung, Hans Lamecker, Hervé Delingette; Subject-specific ligament models: Towards real-time simulation of the knee joint; CBM 2009: Computational Biomechanics for Medicine IV, London, 2009.

Abstract

We present an efficient finite element method to simulate a transversely isotropic non-linear material for ligaments. The approach relies on tetrahedral elements and exploits the geometry to optimize computation of the derivatives of the strain energy. To better support incompressibilty, deviatoric and dilational responses are uncoupled and a penalty term controls volume preservation. We derive stress and elasticity tensors required for implicit solvers and verify our model against the FEBio software using a variety of load scenarios with synthetic shapes. The maximum node positioning error for ligament materials is < 5% for strains under physiological conditions. To generate subject-specific ligament models, we propose a novel technique to estimate fiber orientation from segmented ligament geometry. The approach is based on an automatic centerline extraction and generation of the corresponding diffusion field. We present results for a medial collateral ligament segmented from standard Magnetic Resonance Imaging (MRI) data. Results show the general viability of the method, but also the limitations of current MRI acquisitions. In the future, we hope to employ the presented techniques for real-time simulation of knee surgery.

Mots-clés

  • knee
  • ligament
  • real-time
  • simulation

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3D Anatomical Human (projet INRIA)

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