A Visual Query-Driven Search Engine for Brain Tissue Image Analysis
| dc.contributor.advisor | Roysam, Badrinath | |
| dc.contributor.committeeMember | Prasad, Saurabh | |
| dc.contributor.committeeMember | Mayerich, David | |
| dc.contributor.committeeMember | Nguyen, Hien Van | |
| dc.contributor.committeeMember | Maric, Dragan | |
| dc.creator | Mills, Rachel W | |
| dc.creator.orcid | 0000-0003-2235-5227 | |
| dc.date.accessioned | 2024-01-24T20:11:13Z | |
| dc.date.created | December 2023 | |
| dc.date.issued | 2023-12 | |
| dc.date.updated | 2024-01-24T20:11:13Z | |
| dc.description.abstract | We present a versatile multiscale visual search engine for visual query-driven analysis of whole-slide multiplex IHC scans of brain tissue, without the confines, limitations, and programming needs of conventional script-based image analysis. Our unsupervised machine learning-based method adaptively learns the cytoarchitectural characteristics of provided training images, without any human effort or intervention. Then, visual queries can be submitted by indicating individual cell(s), and/or multicellular tissue patch(es) of interest, upon which the search engine retrieves a rank-ordered spatially mapped list of similar other cells or tissue patches based on similar cell morphologies, protein expression, cytoarchitecture, myeloarchitecture, vasculature, etc. Retrievals from multiple queries can be co-analyzed intuitively to generate complex inferences that would otherwise require sophisticated programming. We envision a broad range of uses, e.g., identifying cell populations, discovering cellular/cytoarchitectural similarities and differences across brain regions, delineating brain regions, fitting/refining/building atlases, delineating cortical cell layers, and proofreading automated image analysis results. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657/16151 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | search engine, multiplex, query-driven, deep learning, person re-id, image search, similarity search, histology, rat brain atlas | |
| dc.title | A Visual Query-Driven Search Engine for Brain Tissue Image Analysis | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2025-12-01 | |
| local.embargo.terms | 2025-12-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |