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  4. Brain micro-vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high-resolution phase-contrast X-ray tomography
 
research article

Brain micro-vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high-resolution phase-contrast X-ray tomography

Patera, Alessandra  
•
Zippo, Antonio G.
•
Bonnin, Anne  
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2021
International Journal Of Imaging Systems And Technology

High-throughput synchrotron-based tomographic microscopy at third generation light sources allows to probe cm-sized samples at micrometer-resolution. In this work, we present an approach to image a full mouse brain. With Indian-ink as a contrast agent, it was possible to obtain 3D distribution of microvessels while a computational framework automatically extracted the morphological and geometrical embedding of the putative vascular systems. Results demonstrate the potentiality of the proposed methodology to visualize and quantify in 3D details of the brain tissue with an image quality and resolution previously unachievable.

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Type
research article
DOI
10.1002/ima.22520
Web of Science ID

WOS:000589608000001

Author(s)
Patera, Alessandra  
Zippo, Antonio G.
Bonnin, Anne  
Stampanoni, Marco
Biella, Gabriele E. M.
Date Issued

2021

Publisher

WILEY

Published in
International Journal Of Imaging Systems And Technology
Volume

31

Issue

3

Start page

1211

End page

1220

Subjects

Engineering, Electrical & Electronic

•

Optics

•

Imaging Science & Photographic Technology

•

Engineering

•

brain vasculature system

•

deep learning

•

x-ray tomography

•

3d

•

reconstruction

•

vasculature

•

microscopy

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CIBM  
Available on Infoscience
December 2, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/173778
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