Dana H. Brooks
Donald M. O'Malley, Leslie Day, Bahram Shafai
Date of Award
Master of Science
Department or Academic Unit
College of Engineering. Department of Electrical and Computer Engineering.
Biomedical engineering, Zebrafish, Electrical engineering
Neurons--Analysis, Diagnostic imaging
Biomedical Engineering and Bioengineering
Vertebrate central nervous systems (CNS) contain hundreds or thousands of distinct nerve cell types with specialized morphologies and functions. As neural systems of the CNS are disrupted in diverse situations, including neurodegenerative diseases, stroke, and spinal cord injury, it is important to analyze and understand the organization and function of these systems and their components, i.e. neurons, in detail. Current imaging technology provides neurobiologists possibility of looking into the brain of an intact vertebrate to observe in vivo activities at the single cell level. However, the huge amount of data generated by these new imaging technologies makes it challenging for a neurobiologist to extract the desired information out of these large datasets. The need for automated neuron detection and analysis techniques in these datasets becomes increasingly important as the number and size of datasets grow at an increasing pace. In this work we describe a method that was developed to detect and identify neurons within 3D confocal z-stacks acquired from the zebrafish brainstem. In our method we first register z-stacks into a normalized space and design a pattern in the normalized space that determines the location of all known neurons in the brainstem. The next step is to segment neurons in the 3D z-sacks using a contour based segmentation method. The algorithm then assigns all segmented neurons to specific locations within the brainstem. The registration requires user interaction while the segmentation and neuron identification stages are fully automated.
Kamali, Matin, "Semiautomatic neuron segmentation and identification in zebrafish brainstem" (2008). Electrical and Computer Engineering Master's Theses. Paper 23. http://hdl.handle.net/2047/d10017926
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