Quantitative Image Analysis - CNRS URA 2582  


  HEADDr. OLIVO-MARIN Jean-Christophe / jcolivo@pasteur.fr
  MEMBERSBERLEMONT Sylvain, Dr de CHAUMONT Fabrice, CHENOUARD Nicolas, Dr DUFOUR Alexandre, LIN Marie-Anne, Dr MEAS-YEDID Vannary, , MARIM Marcio


  Annual Report

The scientific project of the Quantitative Image Analysis (QuIA) unit is to develop image analysis and computer vision tools for the processing and quantification of multi-channel temporal 3D sequences in biological microscopy. Our work over the last years has been centered about developing new algorithms for multi-particle tracking, active contours models, PSF approximations for image deconvolution and image segmentation. It has resulted in powerful tools for spot detection and counting in real-time imaging of virus and genes, movement and shape analysis in 3D+t microscopy and cell growth analysis. These methods and algorithms have been instrumental for the successful achievement of a large number of biological projects to which we collaborated.

Multiple spot tracking in 3D+t microscopy

We have developed a new method to combine image model estimation and kinetic model estimation in order to build an improved multiple particles tracking score in 3D microscopy. The image model takes into account the noise distribution and the shape of the point spread function of the acquisition system in order to build a feature that quantifies the agreement of the image model with an association hypothesis. A score function of association between targets and tracks that uses this feature information and the kinetic information has been built by splitting the measurement vector into a kinetic part and a features related part, and its statistical nature allows us to introduce it naturally in any Bayesian tracking algorithm. A weighting of kinetic and image information was proposed, that is especially indicated to the case of 3D images. We also have proposed a procedure to separate closely spaced targets based on both image and kinetic information when the targets appear fused because of the convolution of the scene by the PSF.

Cell shape and motility analysis

The methodological basis for our work on cell shape and motility is the framework of deformable models or active contours. A major progress made over the last years is the extension of our techniques to 3D+t data. We have taken advantage of the level set method and its relatively easy extension to higher dimensions to include most of the features pre-viously developed for 2D images, including a robust region model and multiple level sets coupled by a non-overlap constraint. In addition, the 3D method takes advantage of cell volume homeostasis to substantially improve the outlining of touching cells. This is an important step towards quantifying the behaviour of cells imaged in 3D environments, e.g. parasites in host tissue.

Laiq.jpg

Fig1: tracking fluorescent beads in 3D+t microscopy images

Fig2: tracking cells with coupled active surfaces



  Publications

Zhang, B., Zerubia, J., and Olivo-Marin, J.-C. (2007) Gaussian approximations of fluorescence microscope point-spread function models, Applied Optics, 46, 10, pp. 1819-1829

Arhel, N., Genovesio, A., Kim, K-A., Miko, S., Perret, E., Olivo-Marin, J.-C., Shorte, S. and Charneau, P. (2006) Quantitative four-dimensional tracking of cytoplasmic and nuclear HIV-1 complexes, Nature Methods, 3, 10, pp. 817-824

Genovesio, A., Liedl, T., Emiliani, V., Parak, W., Coppey-Moisan, M. and Olivo-Marin, J.-C. (2006) Multiple particle tracking in 3D+t microscopy : method and application to the tracking of endocytozed Quantum Dots, IEEE Trans. Image Processing, 15, 5, pp. 1062-1070

Cabal, G., Genovesio, A., Rodriguez-Navarro, S., Zimmer, C., Gadal, O., Lesne, A., Buc, H., Feuerbach-Fournier, F., Olivo-Marin, J.-C., Hurt, E.C., and Nehrbass, U. (2006) SAGA interacting factors confine sub-diffusion of transcribed genes to the nuclear envelope, Nature, 441, pp. 770-773

Zimmer, C. and Olivo-Marin, J.-C (2005) Coupled parametric active contours, IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 11, pp. 1838-1842





Activity Reports 2009 - Institut Pasteur
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