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:
Sep. 10, 2024

Filed:

May. 30, 2022
Applicant:

Carl Zeiss Microscopy Gmbh, Jena, DE;

Inventors:

Alexander Freytag, Erfurt, DE;

Matthias Eibl, Jena, DE;

Christian Kungel, Penzberg, DE;

Anselm Brachmann, Jena, DE;

Daniel Haase, Zoellnitz, DE;

Manuel Amthor, Jena, DE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/26 (2022.01); G06T 5/20 (2006.01); G06T 5/70 (2024.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06V 10/75 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06T 5/20 (2013.01); G06T 5/70 (2024.01); G06T 7/0002 (2013.01); G06T 11/001 (2013.01); G06V 10/273 (2022.01); G06V 10/759 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A method generates an image processing model to calculate a virtually stained image from a microscope image. The image processing model is trained using training data comprising microscope images as input data into the image processing model and target images that are formed via chemically stained images registered locally in relation to the microscope images. The image processing model is trained to calculate virtually stained images from the input microscope images by optimizing an objective function that captures a difference between the virtually stained images and the target images. After a number of training steps, at least one weighting mask is defined using one of the chemically stained images and an associated virtually stained image calculated after the number of training steps. In the weighting mask, one or more image regions are weighted based on differences between locally corresponding image regions in the virtually stained image and in the chemically stained image. Subsequent training considers the weighting mask in the objective function.


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