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. 06, 2021

Filed:

Aug. 21, 2018
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Yefeng Zheng, Princeton Junction, NJ (US);

Zizhao Zhang, Gainesville, FL (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/50 (2006.01); G06T 5/00 (2006.01); G06K 9/62 (2006.01); G06T 7/174 (2017.01); G16H 50/70 (2018.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G16H 50/50 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61B 8/08 (2006.01);
U.S. Cl.
CPC ...
G06T 5/50 (2013.01); G06K 9/6256 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 3/08 (2013.01); G06T 5/006 (2013.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 6/032 (2013.01); A61B 6/5247 (2013.01); A61B 6/563 (2013.01); A61B 8/5261 (2013.01); G06K 2209/05 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10108 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30004 (2013.01);
Abstract

Systems and methods for generating synthesized images are provided. An input medical image of a patient in a first domain is received. A synthesized image in a second domain is generated from the input medical image of the patient in the first domain using a first generator. The first generator is trained based on a comparison between segmentation results of a training image in the first domain from a first segmentor and segmentation results of a synthesized training image in the second domain from a second segmentor. The synthesized training image in the second domain is generated by the first generator from the training image in the first domain. The synthesized image in the second domain is output.


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