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

Journal Article · Wed Apr 19 00:00:00 EDT 2023 · Journal of Physical Chemistry. B

DOI:https://doi.org/10.1021/acs.jpcb.3c00097· OSTI ID:1983732

Boyle, Michael J. ^[1]; Goldman, Yale E. ^[2]; Composto, Russell J. ^[2]

---

1. Univ. of Pennsylvania, Philadelphia, PA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
2. Univ. of Pennsylvania, Philadelphia, PA (United States)

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: National Institutes of Health (NIH); National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1983732
• Report Number(s): LLNL-JRNL-842942; 1065424
• Journal Information: Journal of Physical Chemistry. B, Journal Name: Journal of Physical Chemistry. B Journal Issue: 16 Vol. 127; ISSN 1520-6106
• Publisher: American Chemical Society
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Engineering
Materials Science
Mathematics and Computing
Nanoscience and Nanotechnology