Anatole Chessel
Pièce : 84-20 12
Tél :+33 (0) 16933 5014
anatole.chessel at polytechnique.edu
orcid.org/0000-0002-1326-6305
Research interest
Bioimage informatics for large datasets: how do we handle, quantify, visualise and share large image datasets, large either in number of conditions (high throughput/high content imaging) and/or acquisition size (large-scale light-sheet or block-face microscopy). The aim is to integrate them into biological models to unlock the potential of those data in advancing our understanding of complex biological systems.
Quantitative Geometry Once large microscopy datasets are fully quantified, the results typically take the form of a large number of geometrical data, point cloud, surfaces, curves or trees that represent cell position and shapes, vesicules trajectories or axonal paths. To ease the building of quantitative biology workflows from those data, we are developing GeNePy3D a python quantitave geometry library.
Background
2015-present: MdC at Laboratory for Optics and Biosciences, CNRS / INSERM / Ecole Polytechnique, France
2010–2015 Postdoc with Rafael E. Carazo-Salas, University of Cambridge, Cambridge, UK
2007-2010 Postdoc with Charles Kervrann, INRIA Rennes and Jean Salamero, Insitut Curie, Paris
2004-2007 PhD, with Frederic Cao (INRIA) and Ronan Fablet (IFREMER, Telecom Bretagne) at Ifremer, Brest
Teaching
EA (Specialised Module) for 3rd year student (M1/2): Data science of biological imaging
MODAL (Practical Course) for 2nd year student at Ecole polytechnique (Cellular imaging for the study of the cytoskeleton)
TREX (Practical Course) in quantitative imaging for 3rd year student at Ecole polytechnique (Size hometostasis in yeast)
Master 2 IMALIS, ENS Ulm, 'Bioimage informatics for neuroimaging' wthin the 'Optical Microscopy : principles and applications in Neurosciences' module
Publications
Selected publications:
Lim, S., Beaurepaire, E., Chessel, A. 2023. NU-Net: A Self-Supervised Smart Filter for Enhancing Blobs in Bioimages. In Proc. IEEE International Conference on Computer Vision (ICCV 2023).
Raoux, C., Chessel, A., Mahou, P., Latour, G., Schanne-Klein, MC. (2023). Unveiling the lamellar structure of the human cornea over its full thickness using polarization-resolved SHG microscopy. Light: Science & Applications 12(1) 190 (2023).
Phan, MS., Matho, K., Beaurepaire, E., Livet, J., Chessel, A. 2022. nAdder: A scale-space approach for the 3D analysis of neuronal traces. PLoS Computational Biology 18 (7), e1010211 (2022).
Phan, MS., Chessel, A. 2022. GeNePy3D: a quantitative geometry python toolbox for bioimaging. F1000Research (2021).
Abdeladim, L., et al. 2019. Multicolor multiscale brain imaging with chromatic multiphoton serial microscopy. Nature Commun. (2019).
Osokin, A., Chessel, A., Carazo, S.R.E. and Vaggi, F., 2017. GANs for Biological Image Synthesis. In Proc. IEEE International Conference on Computer Vision (ICCV 2017).
Chessel, A., 2017. An Overview of data science uses in bioimage informatics. Methods.
Williams, E., Moore, J., Li, S.W., Rustici, G., Tarkowska, A., Chessel, A., Leo, S., Antal, B., Ferguson, R.K., Sarkans, U., Brazma, A., Carazo Salas, R.E. and Swedlow, J.R. 2017. Image Data Resource: a bioimage data integration and publication platform. Nature Methods, 14, 775–781 (2017).
Graml, V.*, Studera, X*., Lawson, J.L.*, Chessel, A.*, Geymonat, M., Bortfeld-Miller, M., Walter, T., Wagstaff, L., Piddini, E. and Carazo-Salas, R.E., 2014. A genomic Multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression. Developmental cell, 31(2), pp.227-239.
Chessel, A., Cinquin, B., Bardin, S., Salamero, J. and Kervrann, C., 2009, June. Computational geometry-based scale-space and modal image decomposition: Application to light video-microscopy imaging. In International Conference on Scale Space and Variational Methods (pp. 770-781).