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:
Nov. 22, 2022

Filed:

Mar. 04, 2020
Applicant:

Shanghai United Imaging Healthcare Co., Ltd., Shanghai, CN;

Inventors:

Bin Su, Shanghai, CN;

Yanyan Liu, Shanghai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 11/00 (2006.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61B 8/08 (2006.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); A61B 5/055 (2013.01); A61B 5/7203 (2013.01); A61B 5/7267 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/5258 (2013.01); A61B 8/5269 (2013.01); G06T 5/003 (2013.01); G06T 5/50 (2013.01); G06T 7/0014 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30092 (2013.01); G06T 2207/30101 (2013.01);
Abstract

The present disclosure is related to systems and methods for image processing. The method may include obtaining an image including at least one of a first type of artifact or a second type of artifact. The method may include determining, based on a trained machine learning model, at least one of first information associated with the first type of artifact or second information associated with the second type of artifact in the image. The trained machine learning model may include a first trained model and a second trained model. The first trained model may be configured to determine the first information. The second trained model may be configured to determine the second information. The method may include generating a target image based on at least part of the first information and the second information.


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