The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 13, 2025

Filed:

Dec. 22, 2020
Applicant:

The Regents of the University of California, Oakland, CA (US);

Inventors:

Aydogan Ozcan, Los Angeles, CA (US);

Yair Rivenson, Los Angeles, CA (US);

Hongda Wang, Los Angeles, CA (US);

Yilin Luo, Los Angeles, CA (US);

Kevin De Haan, Los Angeles, CA (US);

Yijie Zhang, Los Angeles, CA (US);

Bijie Bai, Los Angeles, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/00 (2022.01); G06T 7/00 (2017.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06V 20/698 (2022.01); G06T 7/0012 (2013.01); G06V 10/82 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.


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