Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments
@article{Kandel2020PhaseIW, title={Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments}, author={Mikhail Eugene Kandel and Yuchen R. He and Young Jae Lee and Taylor Hsuan-Yu Chen and Kathryn M. Sullivan and Onur Aydin and M. Taher A. Saif and Hyun Joon Kong and Nahil Atef Sobh and Gabriel Popescu}, journal={Nature Communications}, year={2020}, volume={11} }
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live…
55 Citations
Time-lapse Study of Neural Networks Using Phase Imaging with Computational Specificity (PICS)
- Biology
- 2020
The results show that a deep neural network, when trained on phase images with correct fluorescent labels, can correctly learn the necessary morphological information to successfully predict MAP2 and Tau labels, which allows us to classify axons from dendrites in live, unlabeled neurons.
Live-dead assay on unlabeled cells using phase imaging with computational specificity
- BiologyNature communications
- 2022
The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.
Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging.
- PhysicsApplied physics letters
- 2021
Synthetic aperture interference light (SAIL) microscopy is presented as a solution for high-resolution, wide field of view QPI and employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging.
Spatial light interference microscopy: principle and applications to biomedicine.
- BiologyAdvances in optics and photonics
- 2021
This paper reviews spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white- light illumination, and introduces two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography.
Computational interference microscopy enabled by deep learning
- PhysicsAPL photonics
- 2021
This paper proposes using deep learning to produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM, single-shot images as input, and implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image.
High-resolution impedance mapping using electrically activated quantitative phase imaging
- Materials ScienceLight, science & applications
- 2021
The teams led by Eugen Gheorghiu from the International Centre of Biodynamics in Bucharest, Romania, and by Gabriel Popescu from the University of Illinois at Urbana Champaign have developed an instrument that combines phase imaging with alternating voltage perturbations to measure a target’s refractive index and electrical impedance maps that can reveal properties including tumors in human tissue, or defects on coating surfaces.
Single cell capture, isolation, and long‐term in‐situ imaging using quantitative self‐interference spectroscopy
- Chemistry, BiologyCytometry. Part A : the journal of the International Society for Analytical Cytology
- 2021
According to the results, single cells could be trapped, transferred and pushed into the culture chamber with the microfluidic chip, and the refractive index sensitivity of the proposed quantitative imaging method was 0.0282 and the relative error was merely 0.04%.
Monitoring reactivation of latent HIV by label-free gradient light interference microscopy
- BiologybioRxiv
- 2020
DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning
- PhysicsIEEE Transactions on Medical Imaging
- 2021
A deep neural network is proposed and experimentally demonstrated that rapidly improves the resolution of a three-dimensional refractive index map and offers more than an order of magnitude faster regularization performance compared to the conventional iterative method.
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples
- Computer ScienceArXiv
- 2021
This approach highlights that large-scale multiple-scattering models can be leveraged in place of acquiring experimental datasets for achieving highly generalizable deep learning models and devise a new model-based data normalization pre-processing procedure for homogenizing the sample contrast and achieving uniform prediction quality regardless of scattering strength.
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