Computational biomicroscopy: advanced image processing methods to quantify live biological systems

Microscopes have traditionally relied on exquisite optical components. With the advent of image digitization, the output of microscopes is directly amenable to quantitative analysis. Computation plays an essential part in automatizing downstream image analysis procedures. Upstream, the imaging formation process is increasingly regarded as a key part of the end-to-end pipeline, and integrated computational approaches gain a central role via deconvolution, super-resolution and adaptive microscopy. The goal of this proposal is to develop imaging approaches that leverage two classes of computational tools: machine learning approaches and signal processing using sparse approximations. Their application in the general field of computational imaging is highly promising. Specifically, the first aim of the research in this proposal is to overcome resolution limitations in low-photon, three-dimensional fluorescence microscopy by use of machine learning techniques in deconvolution and optimal imaging parameter determination. The second aim will address computational artifacts in digital holographic microscopy, a technique that allows collecting images over wide fields of view and focal ranges using fairly inexpensive hardware devices yet that requires carefully designed computational tools for post processing. There, we propose to work in a generalized sampling framework that can be adapted precisely to experimental acquisition devices while retaining the advantages of classical signal processing implementations (fast Fourier transforms). This framework is also particularly well-adapted for performing non-linear approximations in sparse signal representations. The proposed methods will be implemented and become part of an imaging platform recently co-developed by the applicant, which allows acquiring and disseminating microscopy data and tools in a reproducible research framework. The proposed research projects are thematically related to two ongoing projects on temporal super-resolution microscopy and studies on cardiac development and regeneration, which we plan on integrating in this project, forming a cohesive research program in computational biomicroscopy for the applicant's lab.
Idiap Research Institute
Swiss National Science Foundation
Apr 01, 2018
Sep 30, 2022