Library

eCommons

 

QUANTITATIVE ASSESSMENT OF CEREBRAL MICROVASCULATURE USING MACHINE LEARNING AND NETWORK ANALYSIS

Simple item page
dc.contributor.authorHaft Javaherian, Mohammad
dc.contributor.chairNishimura, Nozomi
dc.contributor.committeeMemberFetcho, Joseph R.
dc.contributor.committeeMemberSchaffer, Chris
dc.contributor.committeeMemberSabuncu, Mert
dc.date.accessioned2019-10-15T15:31:26Z
dc.date.available2019-10-15T15:31:26Z
dc.date.issued2019-05-30
dc.description.abstractVasculature networks are responsible for providing reliable blood perfusion to tissues in health or disease conditions. Volumetric imaging approaches, such as multiphoton microscopy, can generate detailed 3D images of blood vessel networks allowing researchers to investigate different aspects of vascular structures and networks in normal physiology and disease mechanisms. Image processing tasks such as vessel segmentation and centerline extraction impede research progress and have prevented the systematic comparison of 3D vascular architecture across large experimental populations in an objective fashion. The work presented in this dissertation provides complete a fully-automated, open-source, and fast image processing pipeline that is transferable to other research areas and practices with minimal interventions and fine-tuning. As a proof of concept, the applications of the proposed pipeline are presented in the contexts of different biomedical and biological research questions ranging from the stalling capillary phenomenon in Alzheimer’s disease to the drought resistance of xylem networks in various tree species and wood types.
dc.identifier.doihttps://doi.org/10.7298/je8z-6e81
dc.identifier.otherHaftJavaherian_cornellgrad_0058F_11425
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:11425
dc.identifier.otherbibid: 11050394
dc.identifier.urihttps://hdl.handle.net/1813/67412
dc.language.isoen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCrowdsourcing
dc.subjectElectrical engineering
dc.subjectcomputer vision
dc.subjectImage Processing
dc.subjectBiomedical engineering
dc.subjectArtificial intelligence
dc.subjectAlzheimer’s disease
dc.subjectBrain Vasculature
dc.subjectNetwork Analysis
dc.titleQUANTITATIVE ASSESSMENT OF CEREBRAL MICROVASCULATURE USING MACHINE LEARNING AND NETWORK ANALYSIS
dc.typedissertation or thesis
dcterms.licensehttps://hdl.handle.net/1813/59810
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh.D., Biomedical Engineering

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
HaftJavaherian_cornellgrad_0058F_11425.pdf
Size:
8.13 MB
Format:
Adobe Portable Document Format
Download

Collections

Cornell Theses and Dissertations