Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Imaging and Computational Methods for Exploring Sub-cellular Anatomy

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. 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. 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

Acquisition and reconstruction of brain tissue using knife-edge scanning microscopy

A fast method for gathering large-scale data sets through the serial sectioning of brain tissue is described. These data sets are retrieved using knife-edge scanning microscopy, a new technique developed in the Brain Networks Laboratory at Texas A&M University. This technique allows the imaging of tissue as it is cut by an ultramicrotome. In this thesis the development of a knife-edge scanner is discussed as well as the scanning techniques used to retrieve high-resolution data sets. Problems in knife-edge scanning microscopy, such as illumination, knife chatter, and focusing are discussed. Techniques are also shown to reduce these problems so that serial sections of tissue can be sampled at resolutions that are high enough to allow reconstruction of neurons at the cellular level

Adaptive Compressive Sampling for Mid-infrared Spectroscopic Imaging

Fourier transform infrared (FTIR) spectroscopy enables label-free molecular identification and quantification of biological specimens. The resolution of diffraction limited FTIR imaging is poor due to the long optical wavelengths (2.5{\mu}m to 12.5{\mu}m)used and this is particularly limiting in biomedical imaging. Photothermal imaging overcomes this diffraction limit by using a multimodal pump/probe approach. However, these measurements require approximately 1 s per spectrum, making them impractical for large samples. This paper introduces an adaptive compressive sampling technique to dramatically reduce hyperspectral data acquisition time by utilizing both spectral and spatial sparsity. This method identifies the most informative spatial and spectral features and integrates a fast tensor completion algorithm to reconstruct megapixel-scale images and demonstrates speed advantages over FTIR imagin

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions