Jean-Christophe OLIVO-MARIN
Quantitative Image Analysis Unit
Institut Pasteur 25-28 rue du docteur Roux, 75015 Paris
Research area of the Unit
The scientific project of the Quantitative Image Analysis (QuIA) unit is to introduce innovative image processing and mathematical approaches to biological imaging and develop image analysis and computer vision tools for the processing and quantification of multi-channel temporal 3D sequences in biological microscopy. The goal is to automate the quantification of images produced in biological research, and facilitate the understanding of the biological information contained therein. We seek to develop new methods adapted to the specific problems encountered in biological imaging as well as the next generation of computer vision tools that are required to handle the ever more specialized and complex questions posed by biological imaging. Our work over the last years has been centered on developing new algorithms for multi-particle tracking, active contours models, image denoising and reconstruction, and color 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 histological biopsies analysis.
Contribution to the programme
The objective is to create an integrated image analysis solution to address a number of challenges in biological imaging and microscopy, and to develop solutions adapted and adaptable to the broad variety of biological questions and imaging schemes and deal with the data flow from the different imaging modalities. The software packages will manage the needs of end users for exhaustive analysis of bioimaging data sets and for deciphering key steps of biological mechanisms at organ, tissular, cellular and molecular levels through the systematic use of time-lapse+3D microscopy and image processing methods. The new software tools will be used in discovery-driven approaches like cell-based assays for therapeutic research or cellular therapies to integrate molecular and cellular information with morphological and functional data, as well as in hypothesis-driven research to address central cell biology and microbiology questions like intracellular trafficking, immune cell recruitment or multi-cellular signaling.
References over the past 5 years
1.      de Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, Meas-Yedid V, Pankajakshan P, Lecomte T, Le Montagner Y, Lagache T, Dufour A, and Olivo-Marin, J.-C. (2012) Icy: an open bioimage informatics platform for extended reproducible research, Nature Methods, 9, 7, pp. 690-6
2.      Thibeaux, R., Dufour, A., Roux, P., Bernier, M., Baglin, A;C., Frileux, P., Olivo-Marin, J.-C., Guillen, N. and Labruyere, E. (2012) Newly visualized fibrillar collagen scaffolds dictate Entamoeba histolytica invasion route in the human colon, Cellular Microbiology, 22, 5, pp. 1007-15
3.      Orieux, F., Sepulveda, E., Loriette, V., Dubertret, B., and Olivo-Marin, J.-C. (2012) Bayesian Estimation for Optimized Structured Illumination Microscopy, IEEE Trans. Image Processing, 21, 2, pp. 601-14
4.      Dufour, A., Thibeaux, R., Labruyere, E. Guillen, N., and Olivo-Marin, J.-C. (2011) 3D Active Meshes: fast discrete deformable models for cell tracking in 3D time-lapse microscopy, IEEE Trans. Image Processing, 20, 7, pp. 1925-37
5.      Marim, M., Atlan, M., Angelini, E., and Olivo-Marin, J.-C. (2010) Compressed Sensing with off-axis, frequency-shifting holography, Optics Letters, 35, 6, pp. 871-3
6.      Gousset, K., Schiff, E., Langevin, C., Marijanovic, Z., Caputo, A., Browman, DT, Chenouard, N., de Chaumont, F., Martino, A., Enninga, J., Olivo-Marin J.-C., Mannel, D., and Zurzolo, C. (2009) Prions hijack tunneling nanotubes for intercellular spread, Nature Cell Biology, 11, 3, pp. 328-36
7.      Zhang, B., Zerubia, J., and Olivo-Marin, J.-C. (2007) Gaussian approximations of fluorescence microscope point-spread function models, Applied Optics, 46, 10, pp. 1819-29