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:
Jan. 30, 2024

Filed:

Jul. 30, 2020
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Katharina Breininger, Erlangen, DE;

Marcus Pfister, Bubenreuth, DE;

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 34/10 (2016.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G06F 30/27 (2020.01); G16H 70/60 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); G16H 40/63 (2018.01);
U.S. Cl.
CPC ...
A61B 34/10 (2016.02); G06F 30/27 (2020.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); A61B 2034/104 (2016.02); A61B 2034/107 (2016.02); G06T 2207/20081 (2013.01);
Abstract

A system and method for deformation simulation of a hollow organ able to be deformed by the introduction of a medical instrument. The method includes provision of a pre-trained machine-learning algorithm, provision of a 3D medical recording of the hollow organ with surrounding tissue, with the 3D recording having been recorded before the introduction of a medical instrument, segmentation or provision of a segmentation of the 3D medical recording of the hollow organ and establishment or provision of a three-dimensional model of the hollow organ, provision of information about a medical instrument introduced or to be introduced, and simulation of the deformation of the hollow organ to be expected from introduction of the instrument on the basis of the segmented 3D medical recording of the hollow organ and of the surrounding tissue and of the information about the instrument by using the pre-trained machine-learning algorithm.


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