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:
Aug. 18, 2020

Filed:

Sep. 09, 2016
Applicants:

Siemens Healthcare Gmbh, Erlangen, DE;

The Johns Hopkins University, Baltimore, MD (US);

Inventors:

Shaohua Kevin Zhou, Plainsboro, NJ (US);

David Liu, Franklin Park, NJ (US);

Berthold Kiefer, Erlangen, DE;

Atilla Peter Kiraly, Plainsboro, NJ (US);

Benjamin L. Odry, West New York, NY (US);

Robert Grimm, Nuremberg, DE;

Li Pan, Perry Hall, MD (US);

Ihab Kamel, Ellicott City, MD (US);

Assignees:

Siemens Healthcare GmbH, Erlangen, DE;

The Johns Hopkins University, Baltimore, MD (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06K 9/46 (2006.01); G16H 30/40 (2018.01); G06T 11/00 (2006.01); G16H 50/20 (2018.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 8/08 (2006.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/055 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 8/08 (2013.01); G06K 9/4671 (2013.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01); G06T 11/003 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/20081 (2013.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01);
Abstract

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.


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