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. 28, 2019

Filed:

Sep. 15, 2017
Applicant:

Siemens Medical Solutions Usa, Inc., Malvern, PA (US);

Inventors:

Zhennan Yan, South River, NJ (US);

Yiqiang Zhan, Berwyn, PA (US);

Shu Liao, Chester Springs, PA (US);

Yoshihisa Shinagawa, Downingtown, PA (US);

Xiang Sean Zhou, Exton, PA (US);

Matthias Wolf, Coatesville, PA (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/246 (2017.01); G06K 9/62 (2006.01); G06K 9/00 (2006.01); G06N 5/02 (2006.01); G06K 9/78 (2006.01); G06T 5/50 (2006.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G16H 15/00 (2018.01); G06K 9/20 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06T 7/248 (2017.01); G06K 9/00288 (2013.01); G06K 9/6255 (2013.01); G06K 9/6256 (2013.01); G06K 9/78 (2013.01); G06N 5/02 (2013.01); G06T 5/50 (2013.01); G06T 7/0014 (2013.01); G06T 7/97 (2017.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06K 2009/2045 (2013.01); G06K 2209/051 (2013.01); G06N 20/00 (2019.01); G06T 2207/10081 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30004 (2013.01);
Abstract

A framework for automatic retrieval of medical images. In accordance with one aspect, the framework detects patches in a query image volume that contain at least a portion of an anatomical region of interest by using a first trained classifier. The framework determines disease probabilities by applying a second trained classifier to the detected patches, and selects, from the patches, a sub-set of informative patches with disease probabilities above a pre-determined threshold value. For a given patch from the sub-set of informative patches, the framework retrieves, from a database, patches that are most similar to the given image. Image volumes associated with the retrieved patches are then retrieved from the database. A report based on the retrieved image volumes may then be generated and presented.


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