BioImage Informatics 2021
Institut Pasteur, November 29-December 1, 2021

BioImage Informatics 2021 is an annual meeting in the processing, analysis, and extraction of information and knowledge from biological images. This year, the conference will be held virtually from November 29 to December 1, 2021, and will be organized by Jean-Christophe Olivo-Marin (General Chair), Charles Kervrann, Jean Salamero and Jean-Yves Tinevez (Programme Chairs).

BioImage Informatics was established in 2005 and has met annually with key leaders in the field and an annual attendance of several hundreds of computational scientists. Previous recent meetings have been held in Seattle USA 2019, Banff Canada 2017, Singapore 2016, NIST USA 2015, Leuven Belgium 2014, and Dresden Germany 2012. The meeting has been influential in the field and is attended by strong researchers and students interested in this informatics domain. With the advent of machine learning applications in cellular bioimaging science, the significance of these tools and techniques is of increasing value.

Advances in biological cell and tissue labelling and automated microscopy and imaging have revolutionized how biologists visualize molecular, cellular, and compartmental structures and study their respective functions. Huge volumes of multi-dimensional bioimaging data are now being generated in almost every branch of cell biology and neuroscience, particularly through dynamic imaging and electron microscopy applications. The interpretation and analysis of these image datasets in a quantitative, objective, automatic and efficient manner is a major challenge in current computational biology. The field of bioimage informatics also encompasses both hypothesis- and data-driven exploratory approaches, with an emphasis on how to generate biological knowledge and insights.

BioImage Informatics 2021 meeting will include, but not be limited to, the following topics:

  • Advanced analytical solutions for bioimage processing and analysis
  • Statistical spatial analysis of cellular or molecular distributions
  • Applications of machine and deep learning to analysis of cellular structure and related functions
  • Quantification of dynamic images and transport phenomena Automation of data acquisition and analysis
  • Dynamic cell imaging and biological processes
  • Reconstruction and analysis of structure and function of biological networks
  • Registration, correlation and fusion of multimodality data


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