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Title: Machine learning-based pipeline for high accuracy bioparticle sizing
Authors: Luo, Shaobo
Zhang, Yi
Nguyen, Kim Truc
Feng, Shilun
Shi, Yuzhi
Liu, Yang
Hutchinson, Paul
Chierchia, Giovanni
Talbot, Hugues
Bourouina, Tarik
Jiang, Xudong
Liu, Ai Qun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Luo, S., Zhang, Y., Nguyen, K. T., Feng, S., Shi, Y., Liu, Y., Hutchinson, P., Chierchia, G., Talbot, H., Bourouina, T., Jiang, X. & Liu, A. Q. (2020). Machine learning-based pipeline for high accuracy bioparticle sizing. Micromachines, 11(12), 1-12.
Journal: Micromachines
Abstract: High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.
ISSN: 2072-666X
DOI: 10.3390/mi11121084
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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