This paper experimentally examines different configurations of a
multi-camera array microscope (MCAM) imaging technology. The MCAM is
based upon a densely packed array of “micro-cameras” to
jointly image across a large field-of-view (FOV) at high resolution.
Each micro-camera within the array images a unique area of a sample of
interest, and then all acquired data with 54 micro-cameras are
digitally combined into composite frames, whose total pixel counts
significantly exceed the pixel counts of standard microscope systems.
We present results from three unique MCAM configurations for different
use cases. First, we demonstrate a configuration that simultaneously
images and estimates the 3D object depth across a 100×135mm2 FOV at approximately
20 µm resolution, which results in 0.15 gigapixels (GP)
per snapshot. Second, we demonstrate an MCAM configuration that
records video across a continuous 83×123mm2 FOV with twofold increased resolution
(0.48 GP per frame). Finally, we report a third high-resolution
configuration (2 µm resolution) that can rapidly produce
9.8 GP composites of large histopathology specimens.
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.
We demonstrate a multi-lenses microscopic imaging system that records over- lapping fields-of-view for high-efficiency automated specimen analysis. We show both in simulation and experiment how our system can achieve accurate target object detection on overlapped images.
A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient’s COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.
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