US 12,190,572 B2
Droplet processing methods and systems
Nicholas Dayrell-Armes, Cambridgeshire (GB); David Holmes, Cambridgeshire (GB); Frank F Craig, Cambridgeshire (GB); Marian Rehak, Cambridgeshire (GB); Dimitris Josephides, Cambridgeshire (GB); Robert Salter, Cambridgeshire (GB); William Whitley, Cambridgeshire (GB); Sinan Gokkaya, Cambridgeshire (GB); and Raphael Ruis, Cambridgeshire (GB)
Assigned to SPHERE FLUIDICS LIMITED, Cambridge (GB)
Appl. No. 17/266,447
Filed by Sphere Fluidics Limited, Cambridgeshire (GB)
PCT Filed Aug. 6, 2019, PCT No. PCT/GB2019/052206
§ 371(c)(1), (2) Date Feb. 5, 2021,
PCT Pub. No. WO2020/030903, PCT Pub. Date Feb. 13, 2020.
Claims priority of application No. 1812912 (GB), filed on Aug. 8, 2018.
Prior Publication US 2021/0293691 A1, Sep. 23, 2021
Int. Cl. G06V 10/82 (2022.01); B01L 3/00 (2006.01); G01N 15/14 (2006.01); G01N 15/1433 (2024.01); G06F 18/2431 (2023.01); G06T 7/70 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 20/69 (2022.01); G01N 15/10 (2006.01)
CPC G06V 10/82 (2022.01) [B01L 3/502784 (2013.01); G01N 15/1433 (2024.01); G01N 15/1459 (2013.01); G01N 15/1484 (2013.01); G06F 18/2431 (2023.01); G06T 7/70 (2017.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 20/698 (2022.01); B01L 2200/0652 (2013.01); B01L 2200/0673 (2013.01); B01L 2300/0864 (2013.01); G01N 2015/1006 (2013.01); G01N 2015/1486 (2013.01); G01N 2015/1488 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30242 (2013.01)] 4 Claims
OG exemplary drawing
 
1. A method of processing droplets in a microfluidic system, the method comprising:
capturing a sequence of images of a droplet as it passes through a channel in a microfluidic system;
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 of the droplet;
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; and
controlling a microfluidic process performed on the droplet responsive to the estimated number of cells or other entities in the droplet,
wherein processing each image of the sequence of images using the convolutional neural network to count the number of cells or other entities visible in each image of the droplet comprises classifying each image of the droplet into one of a plurality of categories, wherein the categories comprise at least three categories, a category for no cell or other entity in the droplet, a category for just one cell or other entity in the droplet; and at least one category for more than one cell or other entity in the droplet.