U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

ABSTRACT: Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still re

SUBMITTER: Praschl C 

PROVIDER: S-EPMC10569551 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature