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  • Featured in Physics
  • Open Access

Light Microscopy at Maximal Precision

Matthew Bierbaum, Brian D. Leahy, Alexander A. Alemi*, Itai Cohen, and James P. Sethna

  • Department of Physics, Cornell University, Ithaca, New York 14853, USA

  • *Work done while at Cornell; currently affiliated with Google Inc.
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Phys. Rev. X 7, 041007 – Published 13 October, 2017

DOI: https://doi.org/10.1103/PhysRevX.7.041007

Abstract

Microscopy is the workhorse of the physical and life sciences, producing crisp images of everything from atoms to cells well beyond the capabilities of the human eye. However, the analysis of these images is frequently little more accurate than manual marking. Here, we revolutionize the analysis of microscopy images, extracting all the useful information theoretically contained in a complex microscope image. Using a generic, methodological approach, we extract the information by fitting experimental images with a detailed optical model of the microscope, a method we call parameter extraction from reconstructing images (PERI). As a proof of principle, we demonstrate this approach with a confocal image of colloidal spheres, improving measurements of particle positions and radii by 10–100 times over current methods and attaining the maximum possible accuracy. With this unprecedented accuracy, we measure nanometer-scale colloidal interactions in dense suspensions solely with light microscopy, a previously impossible feat. Our approach is generic and applicable to imaging methods from brightfield to electron microscopy, where we expect accuracies of 1 nm and 0.1 pm, respectively.

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Physics Subject Headings (PhySH)

Focus

Modeling Imperfections Boosts Microscope Precision

Published 13 October, 2017

A theoretical model of light spreading and scattering improves precision of position and size measurements made with an optical microscope by as much as 100 times.

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Popular Summary

Microscopy is the workhorse of the physical and life sciences. Centuries of research in optics have provided windows into the world well beyond the limits of the human eye, telling tales from worm locomotion to protein binding. Yet the analysis of these images frequently performs little better than by human inspection. Current automated techniques rely on approaches taken from computer vision that were designed to mimic human visual perception. While rapid, these approaches contain the same biases and systematic errors present in human analyses. Worse, since the algorithms are heuristics discovered through trial and error, there is no systematic way to improve their accuracy.

Here, we present a scientific, methodological approach for analyzing microscopy images. Our technique relies on fitting experimental images to a detailed optical model of the microscope, extracting all the useful information present in the image. As a demonstration of this approach, we improve the accuracy of particle positions and sizes extracted from confocal microscopy images of dense colloidal suspensions by a factor of 10 to 100 over current methods. Finally, we use our method to measure nanometer-scale interactions in dense colloidal suspensions.

Our improvement in accuracy arises without any modification to the microscope itself, allowing researchers to improve the quality of their data by using cheap computing rather than expensive microscope modifications.

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Phys. Rev. X 7, 041007– Published 13 October, 2017
  • Received 8 April 2017
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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