Patent ID: 11893739
Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 26:
27. A method of generating a digitally stained microscopic image of a label-free sample comprising:
providing a trained, deep neural network that is executed by image processing software using one or more processors of a computing device, wherein the trained, deep neural network is trained with a plurality of matched chemically stained images or image patches and their corresponding fluorescence images or image patches of the same sample;
obtaining a first fluorescence image of the sample using a fluorescence microscope and wherein fluorescent light at a first wavelength or wavelength range is emitted from endogenous fluorophores or other endogenous emitters of frequency-shifted light within the sample;
obtaining a second fluorescence image of the sample using a fluorescence microscope and wherein fluorescent light at a second wavelength or wavelength range is emitted from endogenous fluorophores or other endogenous emitters of frequency-shifted light within the sample
inputting the first and second fluorescence images of the sample to the trained, deep neural network; and
the trained, deep neural network outputting the digitally stained microscopic image of the sample that is substantially equivalent to a corresponding brightfield image of the same sample that has been chemically stained.