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

TL;DR

This paper presents a multi-lens microscopic imaging system combined with machine learning to expand the effective field of view, significantly increasing throughput for automated specimen analysis without sacrificing accuracy.

Contribution

It introduces a novel overlapped imaging approach with co-designed analysis software, enabling high-throughput detection of biological features using standard microscopes.

Findings

Achieved multi-fold increase in detection throughput

Maintained high accuracy in counting white blood cells and malaria parasites

Validated approach through both simulation and experimental results

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.