Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Mudugamuwa, Amith; Hettiarachchi, Samith; Melroy, Gehan; Dodampegama, Shanuka; Konara, Menaka; Roshan, Uditha; Amarasinghe, Y. W. R.; Jayathilaka, W.A.D.M.; Wang, Peihong

doi:10.3390/s22186900

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…An alternative droplet detection method suggested by Mudugamuwa et al [34] is thresholding. In their work, they obtain RGB images and select the color band, which produces the largest contrast between the background and the droplets to use for thresholding and follow this process by morphological operations such as erosion and dilation to obtain a smooth droplet interface.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Microdroplet Image Analysis with Deep Learning

Gelado,

Quilodrán-Casas,

Chagot

2023

Micromachines

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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.

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Section: Discussionmentioning

confidence: 99%

Enhancing Microdroplet Image Analysis with Deep Learning

Gelado,

Quilodrán-Casas,

Chagot

2023

Micromachines

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“…A prominent direction of microfluidics for medical diagnostics is the lab-on-a-chip (LOC) technology [4], which is the study of performing laborious and time-consuming laboratory tasks in a single chip [5]. To perform particle manipulations such as mixing, separation, and delivery in a microfluidic environment, various approaches have been investigated [6][7][8]. A promising approach is the development of microrobots that act as individual robot modules and, as a collective microrobotic swarm.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Motion of Magnetically Actuated Bio-Inspired Microrobots

et al. 2022

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Nature consists of numerous solutions to overcome challenges in designing artificial systems. Various actuation mechanisms have been implemented in microrobots to mimic the motion of microorganisms. Such bio-inspired designs have contributed immensely to microscale developments. Among the actuation mechanisms, magnetic actuation is widely used in bio-inspired microrobotic systems and related propulsion mechanisms used by microrobots to navigate inside a magnetic field and are presented in this review. In addition, the considered robots are in microscale, and they can swim inside a fluidic environment with a low Reynolds number. In relation to microrobotics, mimicry of bacteria flagella, sperm flagella, cilia, and fish are significant. Due to the fact that these biological matters consist of different propulsion mechanisms, the effect of various parameters was investigated in the last decade and the review presents a summary that enhances understanding of the working principle of propulsion mechanisms. In addition, the effect of different parameters on the various speeds of the existing microrobots was analyzed to identify their trends. So, the swimming speeds of the microrobots show an upward trend with increasing body length, frequency, magnetic flux density, and helix angle. Microfabrication techniques play a significant role in the microscale because the device designs are highly dependent on the availability of the techniques. The presented microrobots were manufactured by 3D/4D photolithography and rapid prototyping techniques. Proper materials enable effective fabrication of microrobots using the mentioned techniques. Therefore, magnetically active material types, matrix materials, biocompatible and biodegradable materials are presented in this study. Utilizing biocompatible and biodegradable materials avoids adverse effects to the organs that could occur otherwise. In addition, magnetic field generation is significant for the propulsion of such microrobots. We conclude the review with an overview of the biomimicry of microrobots and magnetically actuated robot propulsion.

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“…MEMSs comprise a multidisciplinary field that has rapidly evolved in recent years due to the major advantage of having a small footprint [1]. Advancements in MEMS technology have provided a positive impact on the development of technologies such as micromechanical [2,3], microthermal [4], micromagnetic [5,6], microoptical [7], microchemical [8], and most significantly, microfluidics, which use the behaviors of fluidic flows in micro-channels to perform various tasks [9][10][11]. In the biomedical sector, MEMS-based devices have demonstrated greater potential in disease diagnostics [12][13][14][15], detection and separation of bio-particles [16], and medical treatments [17].…”

Section: Introductionmentioning

confidence: 99%

Formation Techniques Used in Shape-Forming Microrobotic Systems with Multiple Microrobots: A Review

et al. 2022

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Multiple robots are used in robotic applications to achieve tasks that are impossible to perform as individual robotic modules. At the microscale/nanoscale, controlling multiple robots is difficult due to the limitations of fabrication technologies and the availability of on-board controllers. This highlights the requirement of different approaches compared to macro systems for a group of microrobotic systems. Current microrobotic systems have the capability to form different configurations, either as a collectively actuated swarm or a selectively actuated group of agents. Magnetic, acoustic, electric, optical, and hybrid methods are reviewed under collective formation methods, and surface anchoring, heterogeneous design, and non-uniform control input are significant in the selective formation of microrobotic systems. In addition, actuation principles play an important role in designing microrobotic systems with multiple microrobots, and the various control systems are also reviewed because they affect the development of such systems at the microscale. Reconfigurability, self-adaptable motion, and enhanced imaging due to the aggregation of modules have shown potential applications specifically in the biomedical sector. This review presents the current state of shape formation using microrobots with regard to forming techniques, actuation principles, and control systems. Finally, the future developments of these systems are presented.

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Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Cited by 5 publications

References 52 publications

Enhancing Microdroplet Image Analysis with Deep Learning

Enhancing Microdroplet Image Analysis with Deep Learning

A Review on the Motion of Magnetically Actuated Bio-Inspired Microrobots

Formation Techniques Used in Shape-Forming Microrobotic Systems with Multiple Microrobots: A Review

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