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-printed smartphone microscope for less than 100 $ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.
Highlights
In recent years the field of smart microscopy tried to enhance the user-friendliness as well as the image quaility of a standard microscope
Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which 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]
We show that the effect of a varied light source shape, using the pre-trained convolutional neural network (CNN), does improve the phase contrast, and the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements
Summary
In recent years the field of smart microscopy tried to enhance the user-friendliness as well as the image quaility of a standard microscope. The final output of the instrument can be more than what the user sees through the eyepiece. Enhance phase contrast using machine-learning preparation of the manuscript and only provided financial support in the form of authors’ salaries and/or research materials. The specific roles of these authors are articulated in the ‘author contributions’ section. Hereby I declare that Carl Zeiss Microscopy GmbH did not play a role in the design of the study and the experimental setups. CZ supported the study with professional lab equipment, like the microscope and the microscope objective lenses. The contributing authors were free in formulating their working hypothesis and the ways to proof it
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