Automated particle and cell phenotyping using object recognition and tracking based on machine learning algorithms

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Hantos, Gergely B. and Simon, Gergely and Hejda, Matčj and Bernassau, Anne L. and Desmulliez, Marc P. Y.; (2021) Automated particle and cell phenotyping using object recognition and tracking based on machine learning algorithms. In: 2021 IEEE International Ultrasonics Symposium (IUS). IEEE Ultrasonics Symposium . IEEE, CHN, pp. 1-4. ISBN 9781665403559 (https://doi.org/10.1109/IUS52206.2021.9593615)

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Abstract

Knowledge of the acoustic contrast factor of biological entities can provide useful information regarding defects or cell stages in biological applications of microfluidics. It is also a valuable input in the design of acoustic particle manipulators or sorters. To calculate the contrast factor, the required physical properties can be obtained using contact measurements, but these are not desirable as they can damage particles or cells. In indirect approaches, reference particles are employed and the behavior of the unknown particles or cells is compared with that of the reference particles. Here we propose an image recognition-based framework to automate the entire characterization workflow and obtain acoustic contrast factor without intervention. We use 10 micron diameter polystyrene particles as reference and obtain contrast of 6 and 15 micron particle as a proof of concept. Excellent agreement with expected value within 5% is seen for the 15 micron diameter particles.

ORCID iDs

Hantos, Gergely B., Simon, Gergely, Hejda, Matčj ORCID logoORCID: https://orcid.org/0000-0003-4493-9426, Bernassau, Anne L. and Desmulliez, Marc P. Y.;
  • Item type:Book Section
    ID code:80288
    Dates:
    Date
    Event
    13 November 2021
    Published
    16 September 2021
    Published Online
    21 April 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Physics
    Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Physics > Institute of Photonics
    Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:25 Apr 2022 15:50
    Last modified:11 Nov 2024 15:28
    URI:https://strathprints.strath.ac.uk/id/eprint/80288
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