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

• URL: http://arxiv.org/abs/2110.04921v1
• Date: Sun, 10 Oct 2021 22:52:36 GMT
• Title: Increasing a microscope's effective field of view via overlapped imaging and machine learning
• Authors: 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
• Abstract summary: 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.
• Score: 4.23935174235373
• License: http://creativecommons.org/licenses/by/4.0/
• 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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