QUANTITATIVE ASSESSMENT OF CEREBRAL MICROVASCULATURE USING MACHINE LEARNING AND NETWORK ANALYSIS

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.