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

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

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

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

Symmetry properties of the TCC at different illumination configurations.

<p>In (a) the TCC at <i>p</i> = <i>q</i> = 0 gives the partially coherent transfer function for a brightfield and in (b) for a DPC system (b). The green line shows the axis of symmetry. The DPC setup offers odd symmetry which enables phase-contrast.</p

Quantitative and qualitative results produced by the portable microscope.

<p>Quantitatively measured phase of a glass fiber immersed in oil using qDPC mode in (a); Intensity measurements and their corresponding illumination sources using brightfield mode with NA_<i>C</i> = 0.1 (b), NA_<i>C</i> = 0.2 (c), NA_<i>C</i> = 0.5 (d); The computed DPC image in (e), a measurement in Darkfield mode (NA_<i>o</i> < NA_<i>C</i>) in (f), oblique illumination in (g) and the optimized light-source (NA_<i>C</i> = 0.3; magnified for better visualization) using the CNN in (h) using (a) as the input image.</p

Basic architecture of the used CNN.

<p>CNN which takes the complex 2-channel input images and the generated optimized light source parameters as the training data. The learned filters can then be exported to mobile devices i.e. Android smartphones.</p

Asymmetric illumination source enables phase-contrast.

<p>(a) shows the transmission-function <i>t</i>(<i>x</i>) of the sinusoidial phase object and its spectrum which gets filtered in (b) by the WOTF of the brightfield microscope and in (c) by the DPC-system. One clearly sees, that an odd symmetric optical system is capable of transmitting phase information and images the phase-gradient.</p

Contrast measurements of intensity acquisitions of the fiber differently illuminated.

<p>Contrast measurements of intensity acquisitions of the fiber differently illuminated.</p

Rendering and 3D printed model of the microscope.

<p>In (a) CAD rendering of the microscope, where the lens-distances were exported from the ZEMAX raytracing software to assure correct optical relationships. In (b) the fully automated microscope which uses a low-cost projector to quantitatively image the object’s phase. The location of the LCD is visualized as a white chessboard before a fold-mirror couples the light into the condenser.</p