Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning.

ABSTRACT: Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work,

SUBMITTER: Tahir W 

PROVIDER: S-EPMC10521669 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature