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:
Jun. 17, 2025

Filed:

Jul. 31, 2019
Applicant:

Deutsches Krebsforschungszentrum Stiftung Des Öffentlichen Rechts, Heidelberg, DE;

Inventors:

Lena Maier-Hein, Heidelberg, DE;

Sebastian Josef Wirkert, Heidelberg, DE;

Anant Suraj Vemuri, Heidelberg, DE;

Leonardo Antonio Ayala Menjivar, Heidelberg, DE;

Silvia Seidlitz, Heidelberg, DE;

Thomas Kirchner, Heidelberg, DE;

Tim Adler, Heidelberg, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/02 (2006.01); A61B 5/0205 (2006.01); A61B 5/145 (2006.01); A61B 5/1455 (2006.01); A61B 90/00 (2016.01); G06F 18/2413 (2023.01); G06T 7/00 (2017.01); G06V 10/141 (2022.01); G06V 10/46 (2022.01); G06V 10/60 (2022.01); G06V 10/764 (2022.01); G16H 20/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
A61B 5/0037 (2013.01); A61B 5/0002 (2013.01); A61B 5/02042 (2013.01); A61B 5/0205 (2013.01); A61B 5/14546 (2013.01); A61B 5/14551 (2013.01); A61B 5/443 (2013.01); A61B 5/4547 (2013.01); A61B 5/4875 (2013.01); A61B 5/489 (2013.01); A61B 5/4893 (2013.01); A61B 5/7275 (2013.01); A61B 5/749 (2013.01); A61B 90/361 (2016.02); G06F 18/2413 (2023.01); G06T 7/0012 (2013.01); G06V 10/141 (2022.01); G06V 10/60 (2022.01); G06V 10/764 (2022.01); G16H 20/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); A61B 2090/365 (2016.02); G06T 2207/20081 (2013.01); G06V 10/467 (2022.01); G06V 2201/031 (2022.01);
Abstract

Disclosed herein is a method of generating augmented images of tissue of a patient undergoing open treatment, in particular open surgery, wherein each augmented image associates at least one tissue parameter with a region or pixel of the image of the tissue, said method comprising the following steps: estimating a spectral composition of light illuminating a region of interest of the tissue, obtaining one or more multispectral images of the region of interest, applying a machine learning based regressor or classifier to the one or more multispectral images, or an image derived from said multispectral image, to thereby derive one or more tissue parameters associated with image regions or pixels of the corresponding multispectral image, wherein said regressor or classifier has been trained to predict the one or more tissue parameters from a multispectral image under a given spectral composition of illumination, wherein the regressor or classifier employed is made to match the estimated spectral composition of light illuminating said region of interest of the tissue.


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