Literature DB >> 32118978

Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging.

Yunzhao Wu, Yuqi Zhou, Chun-Jung Huang, Hirofumi Kobayashi, Sheng Yan, Yasuyuki Ozeki, Yingli Wu, Chia-Wei Sun, Atsushi Yasumoto, Yutaka Yatomi, Cheng Lei, Keisuke Goda.   

Abstract

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.

Entities:  

Year:  2020        PMID: 32118978     DOI: 10.1364/OE.380679

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  1 in total

1.  Cell morphology-based machine learning models for human cell state classification.

Authors:  Yi Li; Chance M Nowak; Uyen Pham; Khai Nguyen; Leonidas Bleris
Journal:  NPJ Syst Biol Appl       Date:  2021-05-26
  1 in total

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