Enhancing Microdroplet Image Analysis with Deep Learning

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.

Image-Based Feedback of Multi-Component Microdroplets for Ultra-Monodispersed Library Preparation

“…However, the use of image-based feedback can provide control over these parameters using feedback loops to measure the desirable parameter(s) and modify the controlling parameters in response to changes [ 15 , 16 , 17 ]. The use of image analysis to not simply measure but also control aspects of microdroplets and microfluidics has expanded dramatically recently with the improvement in camera framerates, microsecond exposure times allowing real-time image-processing algorithms, and the implementation of machine learning approaches, which even allow for droplet sorting based on image analysis [ 18 , 19 , 20 , 21 , 22 , 23 ].…”