Abstract
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 language | English |
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Pages (from-to) | 331-342 |
Number of pages | 12 |
Journal | Pacific Symposium on Biocomputing |
Volume | 25 |
Issue number | 2020 |
State | Published - 2020 |
Externally published | Yes |
Event | 25th Pacific Symposium on Biocomputing, PSB 2020 - Big Island, United States Duration: 3 Jan 2020 → 7 Jan 2020 |
Bibliographical note
Publisher Copyright:© 2019 The Authors.
Keywords
- Deep learning.
- Microvasculature
- Segmentation
- Two-photon microscopy