denoising



SABILab is a consulting company that aims at supporting scientific research by providing data/bio-image analysis expertise to scientists. It also has an academic research activity.

How Can We Help?

Here are examples of service we can provide:

  • Diagnostic on the whole data production pipeline (data acquisition, image processing, statistical processing)
  • Design and implementation of a data analysis pipeline using open-source software
  • Design and implementation of tailored image processing methods (see examples in my research works page)
  • Training: image analysis, deep learning, statistical processing with R or Pandas (python)

Jean Ollion

Founder of SABILab

ID I was trained as a scientist at Polytechnique then Sorbonne Université with a double expertise in cell biology and bio-image analysis. During both my PhD and Post-doc, I divided my work between experimental work in biology and bio-image analysis. I developed two software: TANGO for 3D analysis of nuclear organization and BACMMAN for high-throughput analysis of Mother Machine
The Mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for single-cell level quantitative analysis and characterization of many cellular processes such as gene expression and regulation, mutagenesis or response to antibiotics.
data.

I am now working as data scientist and bio-image analyst, providing data analysis expertise to scientists. My knowledge and experience in biology facilitate a deep understanding of the scientific problematic and technical stakes behind the data. I also maintain a research activity, and recently developed an original bio-image processing method involving deep learning. I am currently developing a general purpose denoising method based on deep neural networks.

Education & Experience

  • 2016 - 2017: Université Paris Saclay: Deep Learning course from Data Science Master’s degree (external candidate)
  • 2015 - 2019: Laboratoire Jean Perrin CNRS / Sorbonne Université : Post-doctoral fellow: Mutation dynamics in single bacteria cells. Experience in microfluidics, microscopy, image processing and analysis
  • 2010 - 2014: Dynamique et régulation des génomes, National Museum of Natural History: PhD Nuclear organization of centromeric DNA in human cells. Experience in cell biology, microscopy, image processing and analysis
  • 2009 - 2010: Sorbonne Université: Master’s degree Molecular and cellular biology
  • 2006 - 2010: Ecole polytechnique alumni. General scientific training - specialization in biology.

Publications

  1. J. Ollion and C. Ollion, “DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
  2. L. Robert, J. Ollion, and M. Elez, “Real-time visualization of mutations and their fitness effects in single bacteria,” Nature protocols, vol. 14, no. 11, pp. 3126–3143, 2019.
  3. J. Ollion, M. Elez, and L. Robert, “High-throughput detection and tracking of cells and intracellular spots in mother machine experiments,” Nature protocols, vol. 14, no. 11, pp. 3144–3161, 2019.
    https://rdcu.be/bRSze
  4. L. Robert, J. Ollion, J. Robert, X. Song, I. Matic, and M. Elez, “Mutation dynamics and fitness effects followed in single cells,” Science, vol. 359, no. 6381, pp. 1283–1286, 2018.
    https://science.sciencemag.org/content/359/6381/1283.editor-summary
  5. J. Ollion, F. Loll, J. Cochennec, T. Boudier, and C. Escudé, “Proliferation-dependent positioning of individual centromeres in the interphase nucleus of human lymphoblastoid cell lines,” Molecular biology of the cell, vol. 26, no. 13, pp. 2550–2560, 2015.
    https://www.molbiolcell.org/doi/full/10.1091/mbc.E14-05-1002
  6. J. Ollion, J. Cochennec, F. Loll, C. Escudé, and T. Boudier, “Analysis of nuclear organization with TANGO, software for high-throughput quantitative analysis of 3D fluorescence microscopy images,” in The Nucleus, Springer, 2015, pp. 203–222.
    https://link.springer.com/protocol/10.1007%2F978-1-4939-1680-1_16
  7. J. Ollion, J. Cochennec, F. Loll, C. Escudé, and T. Boudier, “TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization,” Bioinformatics, vol. 29, no. 14, pp. 1840–1841, 2013.
    https://academic.oup.com/bioinformatics/article/29/14/1840/231770

Contact

If you require any further information, feel free to contact me