Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Lensfree Fluorescent On-Chip Imaging of Transgenic Caenorhabditis elegans Over an Ultra-Wide Field-of-View

We demonstrate lensfree on-chip fluorescent imaging of transgenic Caenorhabditis elegans (C. elegans) over an ultra-wide field-of-view (FOV) of e.g., >2–8 cm2 with a spatial resolution of ∼10µm. This is the first time that a lensfree on-chip platform has successfully imaged fluorescent C. elegans samples. In our wide-field lensfree imaging platform, the transgenic samples are excited using a prism interface from the side, where the pump light is rejected through total internal reflection occurring at the bottom facet of the substrate. The emitted fluorescent signal from C. elegans samples is then recorded on a large area opto-electronic sensor-array over an FOV of e.g., >2–8 cm2, without the use of any lenses, thin-film interference filters or mechanical scanners. Because fluorescent emission rapidly diverges, such lensfree fluorescent images recorded on a chip look blurred due to broad point-spread-function of our platform. To combat this resolution challenge, we use a compressive sampling algorithm to uniquely decode the recorded lensfree fluorescent patterns into higher resolution images, demonstrating ∼10 µm resolution. We tested the efficacy of this compressive decoding approach with different types of opto-electronic sensors to achieve a similar resolution level, independent of the imaging chip. We further demonstrate that this wide FOV lensfree fluorescent imaging platform can also perform sequential bright-field imaging of the same samples using partially-coherent lensfree digital in-line holography that is coupled from the top facet of the same prism used in fluorescent excitation. This unique combination permits ultra-wide field dual-mode imaging of C. elegans on a chip which could especially provide a useful tool for high-throughput screening applications in biomedical research

Optical Detection and Sizing of Single Nanoparticles Using Continuous Wetting Films

The physical interaction between nano-scale objects and liquid interfaces can create unique optical properties, enhancing the signatures of the objects with sub-wavelength features. Here we show that the evaporation on a wetting substrate of a polymer solution containing sub-micrometer or nano-scale particles creates liquid micro-lenses that arise from the local deformations of the continuous wetting film. These micro-lenses have properties similar to axicon lenses that are known to create beams with a long depth of focus. This enhanced depth of focus allows detection of single nanoparticles using a low magnification microscope objective lens, achieving a relatively wide field-of-view, while also lifting the constraints on precise focusing onto the object plane. Hence, by creating these liquid axicon lenses through spatial deformations of a continuous thin wetting film, we transfer the challenge of imaging individual nano-particles to detecting the light focused by these lenses. As a proof of concept, we demonstrate the detection and sizing of single nano-particles (100 and 200 nm), CpGV granuloviruses as well as Staphylococcus epidermidis bacteria over a wide field of view of e.g., 5.10×3.75 mm(2) using a ×5 objective lens with a numerical aperture of 0.15. In addition to conventional lens-based microscopy, this continuous wetting film based approach is also applicable to lensfree computational on-chip imaging, which can be used to detect single nano-particles over a large field-of-view of e.g., >20-30 mm(2). These results could be especially useful for high-throughput field-analysis of nano-scale objects using compact and cost-effective microscope designs

On-Chip Cytometry using Plasmonic Nanoparticle Enhanced Lensfree Holography

Computational microscopy tools, in particular lensfree on-chip imaging, provide a large field-of-view along with a long depth-of-field, which makes it feasible to rapidly analyze large volumes of specimen using a compact and light-weight on-chip imaging architecture. To bring molecular specificity to this high-throughput platform, here we demonstrate the use of plasmon-resonant metallic nanoparticles to automatically recognize different cell types based on their plasmon-enhanced lensfree holograms, detected and reconstructed over a large field-of-view of e.g., ~24 mm(2)