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

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

Reconstructing High-Definition Infrared Spectroscopic Images Using Adaptive Sampling and Deep Learning

Microscopic analysis of tissue is the current standard for making clinical diagnostic and prognostic decisions. Histology requires the use of chemical stains and dyes to provide contrast in bright-field imaging systems. Standard histological labels include hematoxylin and eosin (H\&E), Masson's trichrome, and a wide range of immunohistochemical stains targeting proteins. Histological image analysis relies on the quantification of various labor-intensive methods, including cell counting, cell localization, and the measurement of tissue microstructures. Improving the performance of clinical histology requires overcoming two significant barriers: (1) automated tissue segmentation and (2) quantification of molecular composition. While various machine-learning approaches attempt to improve image segmentation, these methods are confounded by deviations between image quality and labeling protocols. One potential solution to both problems is spectroscopic imaging, which provides a quantitative image of the tissue sample, greater molecular detail, and a more robust foundation for segmentation. This dissertation proposes and evaluates a framework for performing label-free histological analysis through three major contributions. First, I develop deep learning architectures that dramatically improve the accuracy of histological segmentation. I then leverage similar architectures to synthesize label-free infrared images to corresponding high-resolution bright-field alternatives for histological interpretation. Finally, I develop an adaptive sampling technique with the potential to provide fast sub-cellular imaging using an emerging photothermal infrared imaging technology.Electrical and Computer Engineering, Department o