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Title:Automated detection of ultrastructural features at neuronal synapses
Author(s):Ramesh, Ashwin
Advisor(s):Koyejo, Oluwasanmi
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Vesicle
Detection
Segmentation
Synaptic
Synapse
Neuronal
Ultrastructural
U-Net
Features
Abstract:Synaptic vesicles are the ultracellular structures responsible for carrying chemical messengers known as neurotransmitters from inside the axon of a neuron to the synaptic junction outside. The variation in size and location of these structures is important in the study of their use and reuse in neurons. We propose a method to locate and estimate the diameter of vesicles in electron microscope images of synapses. We train a U-Net inspired model to perform pixel-wise segmentation of the vesicles against background pixels. We then use contour detection on the resulting segmentation maps to determine individual vesicle centers and effective diameters. To our knowledge, there are no baselines in this task so we establish one on an in-house dataset. Our results show that the proposed model performed well on this task.
Issue Date:2020-05-13
Type:Thesis
URI:http://hdl.handle.net/2142/108195
Rights Information:Copyright 2020 Ashwin Ramesh
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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