Translator Disclaimer
Presentation
10 March 2020 Scalable analysis of architecture of brain tissue with label-free imaging and deep learning (Conference Presentation)
Author Affiliations +
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.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
PRESENTATION


SHARE
Advertisement
Advertisement
Back to Top