Automated detection of ultrastructural features at neuronal synapses
Ramesh, Ashwin
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https://hdl.handle.net/2142/108195
Description
Title
Automated detection of ultrastructural features at neuronal synapses
Author(s)
Ramesh, Ashwin
Issue Date
2020-05-13
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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