BioImage InformaticsNODE

The scientific project of the BioImage Analysis (BIA) unit is to develop image analysis and computer vision tools for the processing and quantification of multi-channel temporal 3D sequences in biological microscopy. The topics are centered about the development of new algorithms for multi-particle tracking, deformable models, mathematical imaging and spatial distribution analysis. The group has produced powerful tools for spot detection and counting in real-time imaging of virus and genes, movement and shape analysis in 3D+t microscopy and cell growth analysis. These methods and algorithms have now been regrouped under the open-source and free platform Icy (http://icy.bioimageanalysis.org), which provides a comprehensive framework for extended reproducible research in bioimage informatics. They have been instrumental for the successful achievement of a large number of collaborative biological projects.

Institut Curie is active in image databases and management. The PICT imaging facility is engaged since 2011 with the Strand Life Sci. company in the development of the CID (Curie Image Database)/iManage (supported by Paris-Centre Node). The CID is linked to the “Curie Image Data center” (2x 100Tb Storage equipment and cluster for image processing and analysis). Since December 2013 CID is open to all FBI users of the PICT, under demand and common rules of imaging platforms (web client). iManage is the commercial version (with licensing), offering support to labs, to install and adapt CID on their own microscopy, at their own sites. Plugins to access the CID from Icy (Institut Pasteur) are developed and published on the central repository of Icy. An interface to interoperate with the servers at Institut Curie is under development. Integration of software developed in collaboration with Inria-Serpico (http://mobyleserpico.rennes.inria.fr/) such as ND-SAFIR, Hullkground are now integrated in the CID for automated treatment. Institut Curie, specifically with Serpico’s Team@Inria-Rennes, also develops new algorithm solutions for dynamic events detection and classification, sub-diffraction light microscopy and CLEM approaches.

The Serpico team provides computational methods and mathematical models to automatically extract, organize and model information present in temporal series of images as they are obtained in multidimensional light and cryo-electron microscopy. In the field of membrane traffic, Serpico addresses the following themes in close collaboration with Curie Institute: image superresolution and image denoising to preserve cell integrity (photo-toxicity vs exposure time), information extraction from images and videos in multidimensional microscopy for molecular interaction analysis, spatiotemporal modeling of molecular species and multi-scale architectures, computational simulation and modeling of membrane transport at different scales. In collaboration with UMR 144 and PICT at Institut Curie, the members of Serpico participate in several joint projects (PhD and post-doc supervision, industrial contracts…). They have proposed user-friendly algorithms for processing 3D or 4D data. Other projects are related to instrumentation in microscopy including computational aspects (SERPICO@Mobyle web service) and data management (CID iManage) on the reconstruction and enhancement of images related to subdiffraction light microscopy and multimodal approaches.

The SIMS Group (Signal, Image and Sound) develops its research in the field of statistical signal and image processing. Tackling ill-posed inverse problems such as deconvolution and diffraction tomography is one of our main fields of expertise. We also focus on computational imaging issues, relying on mathematical tools from both optimization and Bayesian simulation fields. The optimal co-design of computational imaging systems is among our growing topics of interest, with the viewpoint of information theory. In the area of fluorescence microscopy, we investigate the superresolution capacity of random illumination microscopy, in collaboration with Institut Fresnel (Marseille) and the Centre for Integrative Biology (Toulouse).

Expertise of the Team:

• Inverse problems
• Computational imaging
• Machine learning

The lab is focused on the algorithms and computation selected by evolution to perform biological decision-making. We address this topic with an interdisciplinary approach mixing statistical physics, Bayesian machine learning, information theory and various experimental biological setups. We are pursuing 4 research axis:

• Probabilistic pipelines and Artificial Intelligence to probe single biomolecule random walks
• Decision-making of biological system
• Amortized inference in Virtual Reality: DIVA + Genuage
• Numerical Methods for temporal networks

Expertise of the Team:

• Probabilistic pipelines for microscopy data analysis
• Virtual reality and augmented reality applied to visualisation and analysis
• Bayesian Inference, amortised inference and physics-based Bayesian induction.

The team develops deep learning models and large scale image and data analysis algorithms. Applications range from basic research in developmental biology and neuroscience to drug discovery in collaboration with the pharma industry. We provide the source code of our methods with all papers we publish.

Expertise of the Team:

• Deep learning
• Image processing & analysis
• Computational biology

The team develops tools in modeling, simulation, data analysis in cell biology and medicine. Recent developments include new super-resolution SPTs analysis.

Expertise of the Team:

• Modeling: biology at multiple scales.
• Simulation: stochastic and deterministic
• Data analysis: trajectories, EEG, electrophysiology data.