Abstract: ABSTRACT: An instrument for processing droplets in a microfluidic system. The instrument captures a time sequence of images of a droplet as it passes through a channel in a microfluidic system. The instrument also processes each image of the sequence of images using a convolutional neural network to count a number of cells or other entities visible in each image the droplet. This involves processing the count of the number of cells or other entities visible in each image of the droplet to determine an estimated number of cells or other entities in the droplet. The instrument controls a microfluidic process performed on the droplet, e.g. droplet dispensing, responsive to the estimated number of cells or other entities in the droplet. The instrument uses the changing orientation and disposition of droplet contents in combination with machine learning to improve monoclonality assurance.

Abstract: A method of processing droplets in a microfluidic system. The method may comprise capturing a time sequence of images of a droplet as it passes through a channel in a microfluidic system. The method may further comprise processing each image of the sequence of images using a convolutional neural network to count a number of cells or other entities visible in each image the droplet. The method may further comprise processing the count of the number of cells or other entities visible in each image of the droplet to determine an estimated number of cells or other entities in the droplet. The method/system may further comprise controlling a microfluidic process performed on the droplet responsive to the estimated number of cells or other entities in the droplet. Implementations of the method use the changing orientation and disposition of droplet contents in combination with machine learning to improve monoclonality assurance.

Abstract: A method of processing droplets in a microfluidic system. The method may comprise capturing a time sequence of images of a droplet as it passes through a channel in a microfluidic system. The method may further comprise processing each image of the sequence of images using a convolutional neural network to count a number of cells or other entities visible in each image the droplet. The method may further comprise processing the count of the number of cells or other entities visible in each image of the droplet to determine an estimated number of cells or other entities in the droplet. The method/system may further comprise controlling a microfluidic process performed on the droplet responsive to the estimated number of cells or other entities in the droplet. Implementations of the method use the changing orientation and disposition of droplet contents in combination with machine learning to improve monoclonality assurance.