Computational Methods for Visualization, Simulation, and Restoration of Fluorescence Microscopy Data

Fluorescence microscopy is an indispensable tool for biology to study the spatio-temporal dynamics of cells, tissues, and developing organisms. Modern imaging modalities, such as light-sheet microscopy, are able to acquire large three- dimensional volumes with high spatio-temporal resolution for many hours or days, thereby routinely generating Terabytes of image data in a single experiment. The quality of these images, however, is limited by the optics of the microscope, the signal-to-noise ratio of acquisitions, the photo-toxic effects of illumination, and the distortion of light by the sample. Additionally, the serial operation mode of most microscopy experiments, where large data sets are first acquired and only afterwards inspected and analyzed, excludes the possibility to optimize image quality during acquisition by automatically adapting the microscope parameters. These limits make certain observations difficult or impossible, forcing trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. This thesis is concerned with addressing several of these challenges with computational methods. First, I present methods for visualizing and processing the volumetric data from a microscope in real-time, i.e. at the acquisition rate of typical experiments, which is a prerequisite for the development of adaptive microscopes. I propose a low-discrepancy sampling strategy that enables the seamless display of large data sets during acquisition, investigate real-time compatible denoising, convolution, and deconvolution methods, and introduce a low-rank decomposition strategy for common deblurring tasks. Secondly, I propose a computational tractable method to simulate the interaction of light with realistically large biological tissues by combining a GPU-accelerated beam propagation method with a novel multiplexing scheme. I demonstrate that this approach enables to rigorously simulate the wave-optical image formation in light-sheet microscopes, to numerically investigate correlative effects in scattering tissues, and to elucidate the optical properties of the inverted mouse retina. Finally, I propose a data-driven restoration approach for fluorescence microscopy images based on convolutional neural networks (Care) that leverages sample and imaging specific prior knowledge. By demonstrating the superiority of this approach when compared to classical methods on a variety of problems, ranging from restoration of high quality images from low signal-to-noise-ratio acquisitions, to projection of noisy developing surface, isotropic recovery from anisotropic volumes, and to the recovery of diffraction-limited structures from widefield images alone, I show that Care is a flexible and general method to solve fundamental restoration problems in fluorescence microscopy