Revealing architectural order with quantitative label-free imaging and deep learning

ABSTRACT: We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in la

SUBMITTER: Syuan-Ming Guo 

PROVIDER: S-BIAD25 | bioimages |

SECONDARY ACCESSION(S): https://github.com/mehta-lab/reconstruct-order

REPOSITORIES: bioimages