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arXiv · 2110.04921

Increasing a microscope's effective field of view via overlapped imaging and machine learning

Xing Yao·Vinayak Pathak·Haoran Xi·Amey Chaware·Colin Cooke·Kanghyun Kim·Shiqi Xu·Yuting Li·Timothy Dunn·Pavan Chandra Konda·Kevin C. Zhou·Roarke Horstmeyer
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Abstract

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.

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Xing Yao, Vinayak Pathak, Haoran Xi, Amey Chaware, Colin Cooke, Kanghyun Kim, Shiqi Xu, Yuting Li, Timothy Dunn, Pavan Chandra Konda, Kevin C. Zhou, Roarke Horstmeyer. 2021-10-10. Increasing a microscope's effective field of view via overlapped imaging and machine learning. https://doi.org/10.1364/oe.445001

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