{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108195"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108195","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automated detection of ultrastructural features at neuronal synapses","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Ramesh, Ashwin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:49Z","date_published":"2020-08-26T23:58:49Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Vesicle","Detection","Segmentation","Synaptic","Synapse","Neuronal","Ultrastructural","U-Net","Features"],"languages":["en"],"rights":["Copyright 2020 Ashwin Ramesh"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108195","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Ramesh, Ashwin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:49Z","2022-08-26T23:58:55Z","2020-05-13","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Vesicle","Detection","Segmentation","Synaptic","Synapse","Neuronal","Ultrastructural","U-Net","Features"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Ashwin Ramesh"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108195"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Ashwin Ramesh, accepted the attached license on 2020-05-12 at 19:48.","The student, Ashwin Ramesh, submitted this Thesis for approval on 2020-05-12 at 20:03.","This Thesis was approved for publication on 2020-05-13 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15370 on 2020-08-25 at 17:31:20","Made available in DSpace on 2020-08-26T23:58:49Z (GMT). No. of bitstreams: 2 RAMESH-THESIS-2020.pdf: 13114758 bytes, checksum: 151b69806aae993f256f0ac16961f07c (MD5) LICENSE.txt: 4210 bytes, checksum: 53666105fa5ae3ac8d99cf87df33f38c (MD5) Previous issue date: 2020-05-13","Embargo set by: Seth Robbins for item 115808 Lift date: 2022-08-26T23:58:55Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automated detection of ultrastructural features at neuronal synapses"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Oluwasanmi"],"dc:creator":["Ramesh, Ashwin"],"dc:date":["2020-08-26T23:58:49Z","2022-08-26T23:58:55Z","2020-05-13","2020-05"],"dc:description":["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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Ashwin Ramesh, accepted the attached license on 2020-05-12 at 19:48.","The student, Ashwin Ramesh, submitted this Thesis for approval on 2020-05-12 at 20:03.","This Thesis was approved for publication on 2020-05-13 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15370 on 2020-08-25 at 17:31:20","Made available in DSpace on 2020-08-26T23:58:49Z (GMT). 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