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. 08, 2023

Filed:

Jul. 21, 2020
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Florin-Cristian Ghesu, Princeton, NJ (US);

Siqi Liu, Princeton, NJ (US);

Awais Mansoor, Potomac, MD (US);

Sasa Grbic, Plainsboro, NJ (US);

Sebastian Vogt, Monument, CO (US);

Dorin Comaniciu, Princeton Junction, NJ (US);

Ruhan Sa, Monmouth Junction, NJ (US);

Zhoubing Xu, Plainsboro, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); G06T 7/62 (2017.01); G06T 7/11 (2017.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06T 7/00 (2017.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
A61B 5/7275 (2013.01); A61B 5/7267 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); G06F 18/214 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/62 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30061 (2013.01); G06V 2201/031 (2022.01);
Abstract

Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.


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