Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

TL;DR

This paper presents a low-cost smartphone microscope that uses machine learning to optimize phase contrast, enabling better visualization of transparent biological samples without expensive equipment.

Contribution

It introduces a novel application of CNNs to optimize light source shapes for phase contrast in a DIY smartphone microscope, enhancing imaging quality at minimal cost.

Findings

Machine learning improves phase contrast in smartphone microscopy.

The system enhances perceived optical resolution without additional optics.

A <$100 3D-printed microscope setup was successfully developed.

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. 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.