François Chung, Ph.D.

Tag: 2009

CBM 2009 - Conference

CBM 2009 – Conference proceeding

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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.

Keywords

  • knee
  • ligament
  • real-time
  • simulation

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3D Anatomical Human (INRIA project)

MICCAI 2009 – Conference

MICCAI 2009 – Conference proceeding

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Publication

François Chung, Hervé Delingette; Multimodal prior appearance models based on regional clustering of intensity profiles; MICCAI 2009: Medical Image Computing and Computer-Assisted Intervention, London, 2009.

Abstract

Model-based image segmentation requires prior information about the appearance of a structure in the image. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), we propose a method based on a regional clustering of intensity profiles that does not rely on an accurate pointwise registration. Our method is built upon the Expectation-Maximization (EM) algorithm with regularized covariance matrices and includes spatial regularization. The number of appearance regions is determined by a novel model order selection criterion. The prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.

References

Publication

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Poster (PDF)
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Multimodal prior (INRIA project)
3D Anatomical Human (INRIA project)

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MICCAI – Medical Image Computing and Computer Assisted Intervention

Multimodal prior

Multimodal prior

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INRIA project @Sophia-Antipolis, France (2009). Model-based image segmentation requires prior information about the appearance of a structure in the image. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAM), we propose a prior based on a regional clustering of intensity profiles that does not rely on accurate pointwise registration.

This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon the Expectation-Maximization (EM) algorithm with regularized covariance matrices and includes spatial regularization. The number of appearance regions is determined by a novel model order selection criterion. The prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.

We tested our method with 7 liver meshes segmented from Computed Tomography (CT) images and 4 tibia meshes segmented from Magnetic Resonance (MR) images. For both structures, outward profiles composed of 10 samples extracted every mm were generated from meshes with around 4000 vertices.

The main advantage of our approach is that appearance regions are extracted without requiring an accurate pointwise registration. Another advantage is that a meaningful prior may be built with very few datasets (in fact one dataset suffices). Furthermore, the prior is multimodal, therefore able to cope with large variation of appearance.

References

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MICCAI 2009 (conference proceeding)
Ph.D. Thesis 2011 (doctoral thesis)

ORASIS 2009 - Conference

ORASIS 2009 – Conference proceeding

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Publication

François Chung, Jérôme Schmid, Olivier Clatz, Nadia Magnenat-Thalmann, Hervé Delingette; Reconstruction 3D des structures anatomiques des membres inférieurs; ORASIS'09: Congrès des jeunes chercheurs en vision par ordinateur, Association Française pour la Reconnaissance et l'Interprétation des Formes (AFRIF), Trégastel, 2009.

Abstract

Dans cet article, nous nous intéressons à la modélisation des structures anatomiques des membres inférieurs telles que les os, les muscles et les tendons. La méthode proposée commence par une acquisition d'images par résonance magnétique (IRM) durant laquelle les membres inférieurs d'un sujet sont scannés. Des modèles 3D sont ensuite générés après une segmentation manuelle des structures anatomiques. Cependant, la surface des modèles générés n'est pas lisse. De plus, les modèles ne sont pas attachés alors qu'ils devraient l'être anatomiquement. Nous décrivons donc les différentes étapes pour contraindre les modèles à être corrects au niveau anatomique et nous discutons de leur validation. L'objectif de cette méthode est de pouvoir réutiliser ces modèles dans des méthodes de segmentation automatique.

Keywords

  • IRM
  • segmentation
  • modélisation 3D
  • membres inférieurs

References

Publication

Published version (HAL)
Preprint version (PDF)
Presentation (PDF)
Bibliographic reference (BibTeX)

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3D reconstruction (INRIA project)
3D Anatomical Human (INRIA project)

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ORASIS’09
AFRIF – Association Française pour la Reconnaissance et l’Interprétation des Formes

3D reconstruction

3D reconstruction

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INRIA project @Geneva, Switzerland (2009). We are interested in the 3D reconstruction of the lower limb anatomical structures. The proposed method starts with a Magnetic Resonance (MR) image acquisition during which the lower limb of a subject is scanned. 3D models are generated after a manual segmentation of the anatomical structures. However, the surface of the models appears not to be smooth and the models are not attached whereas they should be anatomically.

Various consecutive steps are needed to constrain the models to be correct at the anatomical level. The aim of our method is to reuse these models with automatic segmentation methods.

This work is a collaboration between:

  • INRIA Sophia-Antipolis - Institut National de Recherche en Informatique et en Automatique (FR);
  • UNIGE - Université de Genève (CH).

Our modeling approach allowed us to generate most of the anatomical structures of the lower limb, such as bones, muscles and tendons. We were able to create a total of 109 models including the bones, muscles, tendons and skin. Regarding the bones, we modeled 6 in total. More specifically, the hip, femur, patella, tibia, fibula and foot bone. Finally, we modeled 34 muscles in total. For each muscle, we modeled a pair of tendons (proximal and distal) whose role is to attach muscles to bones. The generated models were evaluated and validated by a medical expert.

References

Related articles

ORASIS 2009 (conference proceeding)
3D Anatomical Human (INRIA project)

Learn more

INRIA Sophia-Antipolis – Institut National de Recherche en Informatique et en Automatique

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