Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation
This work addresses the challenge of automated image analysis for high-resolution brain imaging, which is incremental as it extends an existing deep learning segmentation network to support temporal analysis.
The researchers tackled the problem of analyzing extremely sparse and noisy 4D microscopy data to track microvascular dynamics in the brain, resulting in the ability to identify spontaneous arterial dilations and changes in cerebral blood volume over time.
Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution. However, the high imaging rate necessitates fully automated image analysis, whereas tissue turbidity and photo-toxicity limitations lead to extremely sparse and noisy imagery. In this work, we extend a recently proposed deep learning volumetric blood vessel segmentation network, such that it supports temporal analysis. With this technology, we are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.