106 citations · 336 across the 7 of their papers we have counts for
7 papers
HoloStain: Holographic virtual staining of individual biological cells
Yoav N. Nygate, Mattan Levi, Simcha K. Mirsky +6
Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and en…
Integral refractive index imaging of flowing cell nuclei using quantitative phase microscopy combined with fluorescence microscopy
Gili Dardikman, Yoav N. Nygate, Itay Barnea +4
We suggest a new multimodal imaging technique for quantitatively measuring the integral (thickness-average) refractive index of the nuclei of live biological cells in suspension. F…
Six-pack off-axis holography
Moran Rubin, Gili Dardikman, Simcha K. Mirsky +2
We present a new holographic concept, named six-pack holography (6PH), in which we compress six off-axis holograms into a single multiplexed off-axis hologram without loss of magni…
Simultanous two-wavelength phase unwrapping using external module for multiplexing off-axis holography
Nir A. Turko, Natan T. Shaked
We present a dual-wavelength external holographic microscopy module for quantitative phase imaging of 3D structures with extended thickness range. This is done by simultaneous acqu…
Single-exposure full-field multi-depth imaging using low-coherence holographic multiplexing
Lauren Wolbromsky, Nir A. Turko, Natan T. Shaked
We present a new interferometric imaging approach that allows for multiple-depth imaging in a single acquisition, using off-axis low-coherence holographic multiplexing. This techni…
Simultaneous three-wavelength unwrapping using external digital holographic multiplexing module
Nir A. Turko, Pinkie Jacob Eravuchira, Itay Barnea +1
We present an external interferometric setup that is able to simultaneously acquire three wavelengths of the same sample instance without scanning or multiple exposures. This setup…
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set
Moran Rubin, Omer Stein, Nir A. Turko +6
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classificati…