Quantitative Assessment of Cerebral Microvasculature Using Machine Learning and Network Analysis

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Quantitative Assessment of Cerebral Microvasculature Using Machine Learning and Network Analysis "
Haft Javaherian, Mohammad Nishimura, Nozomi

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1104657
Doc. No	:	TLpq2242421573
Main Entry	:	Haft Javaherian, Mohammad
	:	Nishimura, Nozomi
Title & Author	:	Quantitative Assessment of Cerebral Microvasculature Using Machine Learning and Network Analysis\ Haft Javaherian, MohammadNishimura, Nozomi
College	:	Cornell University
Date	:	2019
student score	:	2019
Degree	:	Ph.D.
Page No	:	349
Abstract	:	Vasculature 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.
Subject	:	Artificial intelligence
	:	Biomedical engineering
	:	Electrical engineering