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:
Jan. 02, 2024

Filed:

Mar. 25, 2021
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Youngjin Yoo, Princeton, NJ (US);

Thomas Re, Monroe, NJ (US);

Eli Gibson, Plainsboro, NJ (US);

Andrei Chekkoury, Erlangen, DE;

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/30 (2017.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 7/30 (2017.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30016 (2013.01);
Abstract

Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.


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