Abstract
Recent advances in optical tissue clearing and three-dimensional (3D)
fluorescence microscopy have enabled high resolution in
situ imaging of intact tissues. Using simply prepared
samples, we demonstrate here “digital labeling,” a
method to segment blood vessels in 3D volumes solely based on the
autofluorescence signal and a nuclei stain (DAPI). We trained a
deep-learning neural network based on the U-net architecture using a
regression loss instead of a commonly used segmentation loss to
achieve better detection of small vessels. We achieved high vessel
detection accuracy and obtained accurate vascular morphometrics such
as vessel length density and orientation. In the future, such digital
labeling approach could easily be transferred to other biological
structures.
Funder
American Heart Association
National Institutes of
Health
U.S. Department of Defense
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
1 articles.
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