Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

PloS One
Benedict DiederichRainer Heintzmann

Abstract

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which also allows to measure the phase gradient using the differential phase contrast (DPC) [1, 2] or even the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-pr...Continue Reading

References

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Aug 10, 2017·Scientific Reports·Daeseong JungChulmin Joo

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Citations

Jan 10, 2019·PloS One·Benedict DiederichRainer Heintzmann
Dec 20, 2019·Biomedical Optics Express·Alex MuthumbiRoarke Horstmeyer
Nov 27, 2020·Nature Communications·Benedict DiederichRainer Heintzmann
Mar 17, 2021·FEMS Microbiology Reviews·Stephen J GoodswenJohn T Ellis

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Methods Mentioned

BETA
light microscopy

Software Mentioned

Matlabs
Androids Camera2
Tensorflow
iOS
Matlab
ADAM
ZEMAX
OpenCV4Android

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