During the past two decades, biological imaging has undergone a revolution in the development of new microscopy techniques that allow visualization of tissues, cells, proteins and macromolecular structures at all levels of resolution. Thanks to recent advances in optics, digital sensors and labeling probes, one can now visualize sub-cellular components and organelles at the scale of a few dozens nanometers to several hundreds of nanometers. As a result, fluorescent microscopy and multimodal imaging has become the workhorse of modern biology. All these technological advances in microscopy, created new challenges for researchers in statistical image analysis and machine learning. In this talk, we present statistical and machine learning methods and algorithms to build an integrated imaging approach that bridges the resolution gaps between the molecule and the whole cell, in space and time. We will present statistical AI-based methods for microscopy image restoration, macromolecule localization, molecule dynamics and interaction analysis inside the cell. The proposed image processing methods and algorithms can be applied to a large range of problems in cell imaging and can be integrated in generic image-based workflows, including for high content screening applications.​

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