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:
May. 23, 2017

Filed:

Aug. 28, 2014
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

David Liu, Franklin Park, NJ (US);

Shaohua Kevin Zhou, Plainsboro, NJ (US);

Martin Kramer, Erlangen, DE;

Michael Sühling, Erlangen, DE;

Christian Tietjen, Furth, DE;

Grzegorz Soza, Heroldsberg, DE;

Andreas Wimmer, Forchheim, DE;

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/05 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06T 7/42 (2017.01); A61B 5/055 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/7264 (2013.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 6/5229 (2013.01); G06K 9/624 (2013.01); G06K 9/6214 (2013.01); G06K 9/6256 (2013.01); G06K 9/6259 (2013.01); G06T 7/0016 (2013.01); G06T 7/42 (2017.01); A61B 5/055 (2013.01); A61B 5/4244 (2013.01); A61B 5/4848 (2013.01); A61B 5/7275 (2013.01); A61B 5/742 (2013.01); A61B 2576/00 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/10136 (2013.01); G06T 2207/20012 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20104 (2013.01); G06T 2207/30056 (2013.01); G06T 2207/30096 (2013.01);
Abstract

For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.


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