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Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN.

Michael J Boyle^1,2, Yale E Goldman³, Russell J Composto¹

¹Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

The Journal of Physical Chemistry. B

|April 19, 2023

Summary

Supervised machine learning with mask R-CNN improves particle detection in interferometric scattering microscopy (iSCAT) by reducing false positives. Training with experimental backgrounds enhances accuracy in noisy iSCAT images.

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Area of Science:

Optical Microscopy
Nanotechnology
Machine Learning

Background:

Interferometric scattering microscopy (iSCAT) images nano-objects like nanoparticles and viruses.
High substrate roughness causes background scattering, mimicking nano-objects in iSCAT images.
Traditional algorithms struggle with accurate particle detection in rough iSCAT imaging.

Purpose of the Study:

To enhance particle detection accuracy in iSCAT experiments with significant background scattering.
To develop a machine learning approach for improved object identification in iSCAT.
To reduce false positives caused by background noise in iSCAT data.

Main Methods:

Implemented a mask region-based convolutional neural network (mask R-CNN) for particle detection.
Generated labeled datasets using experimental background images and simulated particle signals.
Employed transfer learning for efficient mask R-CNN training on limited computational resources.

Main Results:

Mask R-CNN trained with experimental backgrounds significantly reduced false positives.
The inclusion of representative backgrounds improved signal differentiation between particles and noise.
The machine learning approach outperformed traditional Haar-like feature detection in noisy iSCAT data.

Conclusions:

Supervised machine learning, specifically mask R-CNN, offers a robust solution for iSCAT particle detection challenges.
A workflow for creating labeled datasets with experimental backgrounds and simulated signals is presented.
This methodology empowers researchers to improve image processing in iSCAT experiments with challenging backgrounds.