Low-cost oblique illumination: an image quality assessment

EuniceScope: Low-Cost Imaging Platform for Studying Microgravity Cell Biology

Microgravity is proven to impact a wide range of human physiology, from stimulating stem cell differentiation to confounding cell health in bones, skeletal muscles, and blood cells. The research in this arena is progressively intensified by the increasing promises of human spaceflights. Considering the limited access to spaceflight, ground-based microgravity-simulating platforms have been indispensable for microgravity-biology research. However, they are generally complex, costly, hard to replicate and reconfigure - hampering the broad adoption of microgravity biology and astrobiology. To address these limitations, we developed a low-cost reconfigurable 3D-printed microscope coined EuniceScope to allow the democratization of astrobiology, especially for educational use. EuniceScope is a compact 2D clinostat system integrated with a modularized brightfield microscope, built upon 3D-printed toolbox. We demonstrated that this compact system offers plausible imaging quality and microgravity-simulating performance. Its high degree of reconfigurability thus holds great promise in the wide dissemination of microgravity-cell-biology research in the broader community, including Science, technology, engineering, and mathematics (STEM) educational and scientific community in the future.

An apparatus for qualitative assessment of the shading ratio of oblique illumination and real-time high-contrast imaging

Oblique illumination imaging can significantly improve the contrast of transparent thin samples. However, in traditional oblique illumination methods, either the condenser is offset or a block is added to the condenser, which makes it complicated and challenged to build a stable oblique illumination imaging. Herein, we present a method to measure the optimal shading ratio of oblique illumination in an inverted microscope, and develop an apparatus for stable high-speed high-contrast imaging with uniform brightness. At optimal shading ratio, the oblique illumination imaging has better imaging quality than differential interference contrast, which characteristic is independent on sample. In oblique illumination with low magnification objective, the images have uneven brightness. According to target brightness, we have developed a brightness unevenness correction algorithm to form uniform background brightness for oblique illumination. Integrating the algorithm with imaging acquisition, corrected oblique illumination microscopy is appropriate to observe living cells with high contrast.

A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms

Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.

Low-power, low-cost urinalysis system with integrated dipstick evaluation and microscopic analysis

We introduce a coupled dipstick and microscopy device for analyzing urine samples. The device is capable of accurately assessing urine dipstick results while simultaneously imaging the microscopic contents within the sample. We introduce a long working distance, cellphone-based microscope in combination with an oblique illumination scheme to accurately visualize and quantify particles within the urine sample. To facilitate accurate quantification, we couple the imaging set-up with a power-free filtration system. The proposed device is reusable, low-cost, and requires very little power. We show that results obtained with the proposed device and custom-built app are consistent with those obtained with the standard clinical protocol, suggesting the potential clinical utility of the device.