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Brain micro‐vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high‐resolution phase‐contrast X‐ray tomography
Author(s) -
Patera Alessandra,
Zippo Antonio G.,
Bonnin Anne,
Stampai Marco,
Biella Gabriele E. M.
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22520
Subject(s) - resolution (logic) , artificial intelligence , tomography , computer science , x ray microtomography , microscopy , phase contrast microscopy , contrast (vision) , computer vision , deep learning , optics , physics
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.