Microfluidic Droplet Detection via Region-Based and Single-Pass Convolutional Neural Networks with Comparison to Conventional Image Analysis Methodologies

As the complexity of microfluidic experiments and the associated image data volumes scale, traditional feature extraction approaches begin to struggle at both detection and analysis pipeline throughput. Deep-neural networks trained to detect certain objects are rapidly emerging as data gathering tools that can either match or outperform the analysis capabilities of the conventional methods used in microfluidic emulsion science. We demonstrate that various convolutional neural networks can be trained and used as droplet detectors in a wide variety of microfluidic systems. A generalized microfluidic droplet training and validation dataset was developed and used to tune two versions of the You Only Look Once (YOLOv3/YOLOv5) model as well as Faster R-CNN. Each model was used to detect droplets in mono- and polydisperse flow cell systems. The detection accuracy of each model shows excellent statistical symmetry with an implementation of the Hough transform as well as relevant ImageJ plugins. The models were successfully used as droplet detectors in non-microfluidic micrograph observations, where these data were not included in the training set. The models outperformed the traditional methods in more complex, porous-media simulating chip architectures with a significant speedup to per-frame analysis times. Implementing these neural networks as the primary detectors in these microfluidic systems not only makes the data pipelining more efficient, but opens the door for live detection and development of autonomous microfluidic experimental platforms.