Assessing the Performance of Autoencoders for Particle Density Estimation in Acoustofluidic Medium: A Visual Analysis Approach

Lucas Massa, Tiago Vieira, Allan Martins, Bruno G. Ferreira

2024

Abstract

Micro-particle density is important for understanding different cell types, their growth stages, and how they respond to external stimuli. In previous work, a Gaussian curve fitting method was used to estimate the size of particles, in order to later calculate their density. This approach required a long processing time, making the development of a Point of Care (PoC) device difficult. Current work proposes the application of a convolutional autoencoder (AE) to estimate single particle density, aiming to develop a PoC device that overcomes the limitations presented in the previous study. Thus, we used the AE to bottleneck a set of particle images into a single latent variable to evaluate its ability to represent the particle’s diameter. We employed an identical physical apparatus involving a microscope to take pictures of particles in a liquid submitted to ultrasonic waves before the settling process. The AE was initially trained with a set of images for calibration. The acquired parameters were applied to the test set to estimate the velocity at which the particle falls within the ultrasonic chamber. This velocity was later used to infer the particle density. Our results demonstrated that the AE model performed much better, notably exhibiting significantly enhanced computational speed while concurrently achieving comparable error in density estimation.

Paper Citation

in Harvard Style

Massa L., Vieira T., Martins A. and G. Ferreira B. (2024). Assessing the Performance of Autoencoders for Particle Density Estimation in Acoustofluidic Medium: A Visual Analysis Approach. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 436-443. DOI: 10.5220/0012364500003660

in Bibtex Style

@conference{visapp24,
author={Lucas Massa and Tiago Vieira and Allan Martins and Bruno G. Ferreira},
title={Assessing the Performance of Autoencoders for Particle Density Estimation in Acoustofluidic Medium: A Visual Analysis Approach},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={436-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012364500003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Assessing the Performance of Autoencoders for Particle Density Estimation in Acoustofluidic Medium: A Visual Analysis Approach
SN - 978-989-758-679-8
AU - Massa L.
AU - Vieira T.
AU - Martins A.
AU - G. Ferreira B.
PY - 2024
SP - 436
EP - 443
DO - 10.5220/0012364500003660
PB - SciTePress