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:
Apr. 02, 2024

Filed:

Dec. 23, 2019
Applicant:

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

Inventors:

Aydogan Ozcan, Los Angeles, CA (US);

Yair Rivenson, Los Angeles, CA (US);

Yichen Wu, Los Angeles, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 15/14 (2006.01); G01N 21/64 (2006.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06T 3/40 (2006.01); G06T 3/4046 (2024.01); G06T 3/4053 (2024.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06V 10/44 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G01N 15/1475 (2013.01); G01N 21/6458 (2013.01); G06F 18/214 (2023.01); G06N 3/08 (2013.01); G06T 3/4046 (2013.01); G06T 3/4053 (2013.01); G06T 5/003 (2013.01); G06T 5/50 (2013.01); G06V 10/454 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01);
Abstract

A fluorescence microscopy method includes a trained deep neural network. At least one 2D fluorescence microscopy image of a sample is input to the trained deep neural network, wherein the input image(s) is appended with a digital propagation matrix (DPM) that represents, pixel-by-pixel, an axial distance of a user-defined or automatically generated surface within the sample from a plane of the input image. The trained deep neural network outputs fluorescence output image(s) of the sample that is digitally propagated or refocused to the user-defined surface or automatically generated. The method and system cross-connects different imaging modalities, permitting 3D propagation of wide-field fluorescence image(s) to match confocal microscopy images at different sample planes. The method may be used to output a time sequence of images (e.g., time-lapse video) of a 2D or 3D surface within a sample.


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