US 12,299,570 B2
Stain-free detection of embryo polarization using deep learning
Cheng Shen, Pasadena, CA (US); Changhuei Yang, Pasadena, CA (US); Adiyant Lamba, Cambridge (GB); and Magdalena D. Zernicka-Goetz, Pasadena, CA (US)
Assigned to California Institute of Technology, Pasadena, CA (US)
Filed by California Institute of Technology, Pasadena, CA (US); and Cambridge Enterprise Limited, Cambridge (GB)
Filed on Jul. 7, 2022, as Appl. No. 17/859,500.
Claims priority of provisional application 63/219,285, filed on Jul. 7, 2021.
Prior Publication US 2023/0027723 A1, Jan. 26, 2023
Int. Cl. G06N 3/08 (2023.01); G06N 3/045 (2023.01); G06T 7/00 (2017.01); G06V 10/82 (2022.01)
CPC G06N 3/08 (2013.01) [G06N 3/045 (2023.01); G06T 7/0012 (2013.01); G06V 10/82 (2022.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30044 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A method of determining embryo polarization comprising:
under control of a hardware processor:
receiving a three-dimensional (3D) image of an embryo;
generating a two-dimensional (2D) image representing the 3D image of the embryo;
determining a before-onset probability that the embryo is before onset of polarization in the 3D image and an after-onset probability that the embryo is after onset of polarization in the 3D image using a convolutional neural network (CNN) with the 2D image as input, wherein the CNN comprises two output nodes, and wherein the two output nodes output the before-onset probability and the after-onset probability; and
determining a label of the embryo as being before or after the onset of polarization in the 3D image using the before-onset probability and the after-onset probability.