Scalable analysis of architecture of brain tissue with label-free imaging and deep learning (Conference Presentation)

Syuan-Ming Guo; Matt Keefe; David Shin; Jenny Folkesson; Anitha Krishnan; Tomasz Nowakowski; Shalin B. Mehta

doi:10.1117/12.2546798

10 March 2020 Scalable analysis of architecture of brain tissue with label-free imaging and deep learning (Conference Presentation)

Syuan-Ming Guo, Matt Keefe, David Shin, Jenny Folkesson, Anitha Krishnan, Tomasz Nowakowski, Shalin B. Mehta

Author Affiliations +

Proceedings Volume 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020; 1125113 (2020) https://doi.org/10.1117/12.2546798
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

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Syuan-Ming Guo, Matt Keefe, David Shin, Jenny Folkesson, Anitha Krishnan, Tomasz Nowakowski, and Shalin B. Mehta "Scalable analysis of architecture of brain tissue with label-free imaging and deep learning (Conference Presentation)", Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 1125113 (10 March 2020); https://doi.org/10.1117/12.2546798

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KEYWORDS

Brain

Tissues

Neuroimaging

Animal model studies

Brain mapping

Data modeling

Optical properties

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