{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113100"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113100","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Phase imaging with computational specificity for cell biology applications","abstract":"Recent advancements in quantitative phase imaging (QPI) and deep learning have opened up an exciting frontier. It has been shown that deep learning methods, with their ability to extract intricate structures from massive raw datasets, can be applied to both interpreting QPI measurements of biological samples and enhancing the imaging capabilities of QPI systems. Phase imaging with computational specificity (PICS), a workflow that combines deep learning and QPI, has recently been developed to nondestructively measure biophysical parameters or markers from label-free samples directly. In this thesis, we present a new non-invasive, high-throughput method built upon the principle of PICS, to detect the cell cycle of live cell clusters. We demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as investigate cell biophysical parameter distribution across different stages of the cell cycle.","abstract_html":"Recent advancements in quantitative phase imaging (QPI) and deep learning have opened up an exciting frontier. It has been shown that deep learning methods, with their ability to extract intricate structures from massive raw datasets, can be applied to both interpreting QPI measurements of biological samples and enhancing the imaging capabilities of QPI systems. Phase imaging with computational specificity (PICS), a workflow that combines deep learning and QPI, has recently been developed to nondestructively measure biophysical parameters or markers from label-free samples directly. In this thesis, we present a new non-invasive, high-throughput method built upon the principle of PICS, to detect the cell cycle of live cell clusters. We demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as investigate cell biophysical parameter distribution across different stages of the cell cycle.","abstract_has_math":false,"creators":["He, Yuchen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Popescu, Gabriel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T21:47:03Z","date_published":"2022-01-12T21:47:03Z","updated_at":"2026-07-22T22:24:53Z","subjects":["quantitative phase imaging","deep learning","phase imaging with computational specificity"],"languages":["en"],"rights":["Copyright 2021 Yuchen He"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113100","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Popescu, Gabriel"]},{"key":"dc:creator","label":"Author","values":["He, Yuchen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T21:47:03Z","2021-07-23","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["quantitative phase imaging","deep learning","phase imaging with computational specificity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Yuchen He"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113100"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Recent advancements in quantitative phase imaging (QPI) and deep learning have opened up an exciting frontier. It has been shown that deep learning methods, with their ability to extract intricate structures from massive raw datasets, can be applied to both interpreting QPI measurements of biological samples and enhancing the imaging capabilities of QPI systems. Phase imaging with computational specificity (PICS), a workflow that combines deep learning and QPI, has recently been developed to nondestructively measure biophysical parameters or markers from label-free samples directly. In this thesis, we present a new non-invasive, high-throughput method built upon the principle of PICS, to detect the cell cycle of live cell clusters. We demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as investigate cell biophysical parameter distribution across different stages of the cell cycle.","Submission original under an indefinite embargo labeled 'Open Access'. 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Phase imaging with computational specificity (PICS), a workflow that combines deep learning and QPI, has recently been developed to nondestructively measure biophysical parameters or markers from label-free samples directly. In this thesis, we present a new non-invasive, high-throughput method built upon the principle of PICS, to detect the cell cycle of live cell clusters. We demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as investigate cell biophysical parameter distribution across different stages of the cell cycle.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Yuchen He, accepted the attached license on 2021-07-22 at 16:54.","The student, Yuchen He, submitted this Thesis for approval on 2021-07-22 at 17:05.","This Thesis was approved for publication on 2021-07-23 at 14:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17074 on 2022-01-12 at 12:46:56","Made available in DSpace on 2022-01-12T21:47:03Z (GMT). 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