Microvascular dynamics from 4d microscopy using temporal segmentation

Shir Gur, Lior Wolf, Lior Golgher, Pablo Blinder

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Recently developed methods for rapid continuous volumetric two-photon microscopy facili-tate 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 vol-umetric 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.

Original languageAmerican English
Pages (from-to)331-342
Number of pages12
JournalPacific Symposium on Biocomputing
Issue number2020
StatePublished - 2020
Externally publishedYes
Event25th Pacific Symposium on Biocomputing, PSB 2020 - Big Island, United States
Duration: 3 Jan 20207 Jan 2020

Bibliographical note

Publisher Copyright:
© 2019 The Authors.


  • Deep learning.
  • Microvasculature
  • Segmentation
  • Two-photon microscopy


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