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Proceedings Paper

Scalable analysis of architecture of brain tissue with label-free imaging and deep learning (Conference Presentation)
Author(s): Syuan-Ming Guo; Matt Keefe; David Shin; Jenny Folkesson; Anitha Krishnan; Tomasz Nowakowski; Shalin B. Mehta

Paper Abstract

Facile analysis of the architecture of the mammalian brain is key to understanding how brain function emerges during development and dysregulated in disorders including neurodegeneration. Immunolabeling of mammalian brain tissue, especially scarce human brain tissue, is time-consuming, can introduce sample-to-sample variation, and is not compatible with live imaging. We report joint optimization of polarization-resolved label-free imaging and deep learning to map brain architecture. We visualize diverse structures in human brain tissue by mapping optical properties of density, birefringence, orientation, and scattering. We design computationally efficient variants of U-Nets to predict tract distribution and cell types from intrinsic optical properties of the tissue.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 1125113 (10 March 2020); doi: 10.1117/12.2546798
Show Author Affiliations
Syuan-Ming Guo, Chan Zuckerberg Biohub (United States)
Matt Keefe, Univ. of California, San Francisco (United States)
David Shin, Univ. of California, San Francisco (United States)
Jenny Folkesson, Chan Zuckerberg Biohub (United States)
Anitha Krishnan, Chan Zuckerberg Biohub (United States)
Tomasz Nowakowski, Univ. of California, San Francisco (United States)
Chan Zuckerberg Biohub (United States)
Shalin B. Mehta, Chan Zuckerberg Biohub (United States)


Published in SPIE Proceedings Vol. 11251:
Label-free Biomedical Imaging and Sensing (LBIS) 2020
Natan T. Shaked; Oliver Hayden, Editor(s)

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