Large-Scale Segmentation of DNA-Labeled Brain Microstructures Using Deep Semantic Preprocessing

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dc.contributor.advisorMayerich, David
dc.contributor.committeeMemberRoysam, Badrinath
dc.contributor.committeeMemberNguyen, Hien Van
dc.contributor.committeeMemberChen, Guoning
dc.contributor.committeeMemberEriksen, Jason
dc.creatorSaadatifard, Laila
dc.creator.orcid0000-0001-5773-7386
dc.date.accessioned2020-01-04T00:38:43Z
dc.date.createdMay 2019
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.date.updated2020-01-04T00:38:44Z
dc.description.abstractMapping brain architecture is important for understanding neurological function and behavior, particularly when studying neurodegenerative diseases. High throughput microscopy, such as \textit{knife-edge scanning microscopy} (KESM) and \textit{milling with ultraviolet excitation} (MUVE), provides the potential for whole organ imaging at microscopic resolution. The size and complexity of the resulting images makes manual annotation impractical and automatic segmentation challenging. Densely-packed cells with heterogeneous sizes and shapes, combined with complex interconnected vascular networks, pose a problem for current localization and segmentation algorithms. In addition, large teravoxel data sizes necessitate the development of fast algorithms that are largely unsupervised. In this dissertation, a fast and robust cell and microvascular segmentation framework is implemented to quantify tissue microstructure in large microscopy images. The proposed method is validated using thionine-stained rat brain tissue. Deep fully-convolutional neural networks are used as a pre-processing step to enhance the contrast of cellular and vascular components in the thionine-stained data. In this dissertation, a deep and densely-connected fully-convolutional encoder-decoder is designed for pixelwise classification. The trained network reliably distinguishes between cellular and vascular structures. Then a GPU-based cell localization algorithm is designed to identify the three-dimensional positions of cells. It is demonstrated that the proposed ``deep preprocessing'' framework significantly improves the accuracy of relative to the brain microvasculature.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Saadatifard, Leila, Louise C. Abbott, Laura Montier, Jokubas Ziburkus, and David Mayerich. "Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting." Frontiers in neuroanatomy 12 (2018): 28.
dc.identifier.urihttps://hdl.handle.net/10657/5715
dc.language.isoeng
dc.rightsThe 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. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectCellular/vascular
dc.subjectSegmentation
dc.subjectCNN
dc.subjectGPU
dc.titleLarge-Scale Segmentation of DNA-Labeled Brain Microstructures Using Deep Semantic Preprocessing
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-05-01
local.embargo.terms2021-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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