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

Low loss coatings for the VIRGO large mirrors

présentée par L. PinardThe goal of the VIRGO program is to build a giant Michelson type interferometer (3 kilometer long arms) to detect gravitational waves. Large optical components (350 mm in diameter), having extremely low loss at 1064 nm, are needed. Today, the Ion beam Sputtering is the only deposition technique able to produce optical components with such performances. Consequently, a large ion beam sputtering deposition system was built to coat large optics up to 700 mm in diameter. The performances of this coater are described in term of layer uniformity on large scale and optical losses (absorption and scattering characterization). The VIRGO interferometer needs six main mirrors. The first set was ready in June 2002 and its installation is in progress on the VIRGO site (Italy). The optical performances of this first set are discussed. The requirements at 1064 nm are all satisfied. Indeed, the absorption level is close to 1 ppm (part per million), the scattering is lower than 5 ppm and the R.M.S. wavefront of these optics is lower than 8 nm on 150 mm in diameter. Finally, some solutions are proposed to further improve these performances, especially the absorption level (lower than 0.1 ppm) and the mechanical quality factor Q of the mirrors (thermal noise reduction)

Bacterially induced dolomite precipitation in anoxic culture experiments

International audienceTo study the process of microbial-mediated dolomite formation, growth experiments were carried out with selected bacterial cultures under anoxic environmental conditions simulating those found in Lagoa Vermelha, a hypersaline lagoon in Brazil where dolomite precipitation occurs. Specifically, we report the isolation of a particular strain of sulfate-reducing bacteria, LVform6, from Lagoa Vermelha sediment, which apparently promotes the formation of nonstoichiometric dolomite. Sulfate-reducing bacteria grown in a synthetic liquid medium produced dolomite during 30 days incubation at 30 °C. The precipitates have morphologies similar to those observed in Lagoa Vermelha sediment. Our results demonstrate that sulfate-reducing bacteria can influence dolomite precipitation under controlled lowtemperature, anoxic conditions, and imply that anaerobic microorganisms can play an important role in carbonate sedimentation. They may have been particularly significant in Earth's earliest history when a more reducing atmosphere existed

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

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

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