The ability to create large-scale high-resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function. This is particularly important for brain tissue, where the majority of function occurs at the cellular and sub-cellular level. However, reconstructing tissue at sub-cellular resolution is a complex problem that requires new methods for imaging and data analysis. #R##N#In this dissertation, I describe a prototype microscopy technique that can image large volumes of tissue at sub-cellular resolution. This method, known as Knife-Edge Scanning Microscopy (KESM), has an extremely high data rate and can capture large tissue samples in a reasonable time frame. We can therefore image complete systems of cells, such as whole small animal organs, in a matter of days. #R##N#I then describe algorithms that I have developed to cope with large and complex data sets. These include methods for improving image quality, tracing filament networks, and constructing high-resolution anatomical models. These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high-throughput microscopy data. The resulting models of large-scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques.
['John Keyser', 'David Mayerich']
Imaging and computational methods for exploring sub-cellular anatomy