US 12,327,362 B2
Method and system for digital staining of label-free fluorescence images using deep learning
Aydogan Ozcan, Los Angeles, CA (US); Yair Rivenson, Los Angeles, CA (US); Hongda Wang, Los Angeles, CA (US); and Zhensong Wei, Los Angeles, CA (US)
Assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed by THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed on Dec. 18, 2023, as Appl. No. 18/543,168.
Application 18/543,168 is a continuation of application No. 17/041,447, granted, now 11,893,739, previously published as PCT/US2019/025020, filed on Mar. 29, 2019.
Claims priority of provisional application 62/651,005, filed on Mar. 30, 2018.
Prior Publication US 2024/0135544 A1, Apr. 25, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/11 (2017.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01)
CPC G06T 7/11 (2017.01) [G06F 18/2155 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01)] 24 Claims
OG exemplary drawing
 
1. A method of generating a digitally stained microscopic image comprising:
providing a neural network using one or more processors of a computing device, wherein the neural network is trained with a plurality of chemically stained images or image patches matched with corresponding label-free images or image patches of training samples;
obtaining one or more label-free images of a label-free test sample using a fluorescence microscope and one or more excitation light sources, wherein the detected light is emitted from at least one endogenous fluorophore or at least one endogenous emitter of the label-free test sample, and wherein each of the one or more label-free images comprises a single image per each of one or more channels;
inputting the one or more label-free images of the label-free test sample to the neural network; and
outputting the digitally stained microscopic image of the label-free test sample via the neural network.