Gelado, Sofia H;
Quilodrán-Casas, César;
Chagot, Loïc;
(2023)
Enhancing Microdroplet Image Analysis with Deep Learning.
Micromachines
, 14
(10)
, Article 1964. 10.3390/mi14101964.
Preview |
Text
micromachines-14-01964-v2.pdf - Published Version Download (3MB) | Preview |
Abstract
Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurate detection and measurement of droplet diameters and the image restoration of low-resolution images. This study demonstrates that the Segment Anything Model (SAM) provides superior detection and reduced droplet diameter error measurement compared to the Circular Hough Transform, which is widely implemented and used in microfluidic imaging. SAM droplet detections prove to be more robust to image quality and microfluidic images with low contrast between the fluid phases. In addition, this work proves that a deep-learning super-resolution network MSRN-BAM can be trained on a dataset comprising of droplets in a flow-focusing microchannel to super-resolve images for scales ×2, ×4, ×6, ×8. Super-resolved images obtain comparable detection and segmentation results to those obtained using high-resolution images. Finally, the potential of deep learning in other computer vision tasks, such as denoising for microfluidic imaging, is shown. The results show that a DnCNN model can denoise effectively microfluidic images with additive Gaussian noise up to σ = 4. This study highlights the potential of employing deep-learning methods for the analysis of microfluidic images.
Type: | Article |
---|---|
Title: | Enhancing Microdroplet Image Analysis with Deep Learning |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/mi14101964 |
Publisher version: | https://doi.org/10.3390/mi14101964 |
Language: | English |
Additional information: | Copyright: © 2023 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 (https:// creativecommons.org/licenses/by/ 4.0/). |
Keywords: | microdroplets; microfluidics; deep learning; computer vision; image processing |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
URI: | https://discovery.ucl.ac.uk/id/eprint/10179912 |
Archive Staff Only
View Item |