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Modeling and digital imaging
 research groups

Keywords :Modeling, image analysis, spatial statistics, applied mathematics, software development

Doctoral school affiliation : ED 435 ABIES

Contacts :

Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech-ERL3559 CNRS
Bâtiment 2
INRA Centre de Versailles-Grignon
Route de St-Cyr (RD10)
78026 Versailles Cedex France
Tel : +33 (0)1 30 83 30 00 - fax : +33 (0)1 30 83 33 19



Summary :

Biological imaging, in particular fluorescence microscopy and its derivatives, is a tool of choice for analyzing and modeling structures and functions in biological systems. However, quantitative and systematic approaches for the processing, analysis, and integration of biological image data are still rather limited in comparison with their massive production. Bridging this gap is at present one of the major challenge in biological imaging.

In this context, our group develops methods, algorithms and tools for processing, analyzing and modeling data generated, primarily, by biological imaging techniques. We aim, on the one hand, at unraveling principles of spatial organization and, on the other hand, at understanding the factors and mechanisms that subtend these principles. Our activity is thus at the intersection of image processing and analysis, spatial statistics, and mathematical and computational modeling.

We develop our activity through several collaborating projects with biologist groups. Our main projects are on the understanding and the modeling of plant developmental mechanisms, on the functional architecture of the cell nucleus and its implication in the regulation of genome expression, on the modeling of intra-cellular organization of endomembrane compartments in relation with cell growth and polarity, and on the histological characterization of model plants. Other projects, in collaboration with neurobiologists, underline the generic and transversal dimension of the activities of the group.


Main Results :
Image processing and reconstruction of 3D models. We developed a collection of tools for reconstructing, from 3D images, geometric models of biological structures. Our algorithms thus encompass image enhancement (attenuation correction for confocal microscopy images, filtering ; Biot et al., 2008, Legland et al., 2010) and segmentation (spot dectection, active contours ; Biot et al., 2008) as well as surface reconstruction (Maschino et al., 2006 ; Burguet et al., 2011a).
Registration and spatial normalization of imaging data. We developed an algorithmic pipeline for integrating into a single and average model a set of individual models, taking into account the inter-individual morphological fluctuations. Our method combines registration, averaging, and non-linear deformation of geometrical models (Maschino et al., 2006 ; Andrey et al., 2008). We are also interested in designing methods for spatially normalizing iconic data (coll. J.-M. Bonny, INRA Theix ; Lehallier et al., 2011).
Statistical mapping of spatial distributions. We develop methods for integrating collections of normalized positions of objects of interest into 2D or 3D statistical maps (Burguet et al., 2011b). We have thus shown the existence of specific spatial repartitions in varied systems at different scales : endomembrane compartments in Arabidopsis cells (coll. S. Vernhettes, IJPB et J.-D. Faure, IJPB), neuronal populations in small animal brain (coll. Y. Maurin, INRA Jouy-en-Josas and N. Darcel, AgroParisTech ; Burguet et al., 2009 ; Schwarz et al., 2010), vascular bundles in maize stems (coll. V. Méchin, IJPB ; Legland et al., 2011).
Statistical modeling of spatial distributions. We develop a statistical spatial modeling approach for unraveling, testing, and quantifying principles of organization in biological systems. We have for example shown that, in the nuclei of Arabidopsis leaf cells, chromocenters are distributed more regularly than under a completely random repartition (coll. V. Gaudin, IJPB ; Andrey et al., 2010).
Integrated, user-friendly software for 3D reconstruction and modeling. To make available for biologist users our 3D image processing, analysis and modeling algorithms and methods, we develop the Free-D software (Andrey & Maurin, 2005; Biot et al., 2011). See the dedicated web page for further information:

Computational resources :

cell-division-model: an executable version of the model we have developed to simulate divisions at arbitrary volume-ratios in arbitrary 3D cell shapes. The program takes as input a binary 3D mask of a mother cell and simulates its partitioning at the specified volume-ratio. This model was used to reveal a geometrical rule that explains the cell division patterns during early embryogenesis in Arabidopsis thaliana. This work is currently under review.
[Download] The package provides the C++ code of the model core as well as an executable file (Linux Ubuntu 16.04 64-bits).


Selected Publications :

Fernandes JB, Duhamel M, Seguéla-Arnaud M, Froger N, Girard C, Choinard S, Solier V, De Winne N, De Jaeger G, Gevaert K, Andrey P, Grelon M, Guerois R, Kumar R, Mercier R (2018). FIGL1 and its novel partner FLIP form a conserved complex that regulates homologous recombination. PLoS Genetics

Arpon J, Gaudin V, Andrey P (in press). A method for testing random spatial model on nuclear object distributions. Methods in Molecular Biology, Special Issue on "Plant Chromatin Dynamics".

Arganda-Carreras I, Andrey P (2017). Designing Image Analysis Pipelines in Light Microscopy: A Rational Approach. Methods Mol Biol, 1563, 185-207.

Biot E, Cortizo M, Burguet J, Kiss A, Oughou M, Maugarny-Calès A, Gonçalves B, Adroher B, Andrey P, Boudaoud A, Laufs P (2016). Multiscale quantification of morphodynamics: MorphoLeaf, software for 2-D shape analysis. Development, 143, 3417-3428

Biot E, Crowell E, Burguet J, Höfte H, Vernhettes S, Andrey P (2016). Strategy and software for the statistical spatial analysis of 3D intracellular distributions. Plant Journal, 87, 230-42.

Legland D, Arganda-Carreras I, Andrey P (2016). MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics, 32, 3532-3534.

Poulet A, Arganda-Carreras I, Legland D, Probst AV, Andrey P, Tatout C (2015). NucleusJ: an ImageJ plugin for quantifying 3D images of interphase nuclei. Bioinformatics, 31, 1144-6.

Burguet J, Andrey P (2014). Statistical comparison of spatial point patterns in biological imaging. PLoS One, 9, e8775 (on line).

Gul-Mohammed J, Arganda-Carreras I, Andrey P, Galy V, Boudier T (2014). A generic classification-based method for segmentation of nuclei in 3D images of early embryos. BMC Bioinformatics, 15, 9 (pdf)

Legland D, Beaugrand J (2013). Automated clustering of lignocellulosic fibres based on morphometric features and using clustering of variables. Industrial Crops and Products, 45, 253-261 (abstract)

Burguet J, Mailly P, Maurin Y, Andrey P (2011a). Reconstructing the three-dimensional surface of a branching and merging biological structure from a stack of coplanar contours. Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011): From Nano to Macro, 602-605.

Burguet J, Maurin Y, Andrey P (2011b). A method for modeling and visualizing the three-dimensional organization of neuron populations from replicated data: properties, implementation and illustration.
Pattern Recognition Letters, 32, 1894-1901.

Andrey P, Kiêu K, Kress C, Lehmann G, Tirichine L, Liu Z, Biot E, Adenot P.-G, Hue-Beauvais C, Houba-Hérin N, Duranthon V, Devinoy E, Beaujean N, Gaudin V, Maurin Y, Debey P (2010). Statistical analysis of 3D images detects regular spatial distributions of centromeres and chromocenters in animal and plant nuclei.
PLoS Computational Biology, 6, e1000853.

Legland D, Guillon F, Kiêu K, Bouchet B, Devaux M.-F. (2010). Stereological Estimation of cell wall density of DR12 tomato mutant using three-dimensional confocal imaging.
Annals of Botany, 105, 265-276.

Legland D, Devaux M.-F., Guillon F. (2011). Spatial normalisation of maize stem vascular bundles for cartography of their density. Proc. of the 13th International Congress For Stereology, Tsinghua University, Beijing, China Oct. 19-23.

Gaudin V, Andrey P, Devinoy E, Kress C, Kiêu K, Beaujean N, Maurin Y, Debey P (2009). Modeling the 3D functional architecture of the nucleus in animal and plant kingdoms.
C. R. Biologies, 332, 937-946.

Biot E, Crowell E, Höfte H, Maurin Y, Vernhettes S, Andrey P (2008). A new filter for spot extraction in N-dimensional biological imaging. Fifth IEEE International Symposium on Biomedical Imaging (ISBI'08):
From Nano to Macro, 975-978.

Andrey P, Maschino E, Maurin Y (2008). Spatial normalisation of three-dimensional neuroanatomical models using shape registration, averaging, and warping. Fifth IEEE International Symposium on Biomedical Imaging (ISBI'08):
From Nano to Macro, 1183-1186.

Maschino E, Maurin Y, Andrey P (2006). Joint registration and averaging of multiple 3D anatomical surface models.
Computer Vision and Image Understanding, 101, 16-30.

Andrey P, Maurin Y (2005). Free-D: an integrated environment for three-dimensional reconstruction from serial sections.
Journal of Neuroscience Methods, 145, 233-244.



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