Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning

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.