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. 03, 2022

Filed:

Sep. 10, 2018
Applicant:

The General Hospital Corporation, Boston, MA (US);

Inventors:

Synho Do, Lexington, MA (US);

Florian Fintelmann, Boston, MA (US);

Hyunkwang Lee, Boston, MA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/30 (2018.01); A61B 34/10 (2016.01); G06T 7/11 (2017.01); G06T 7/155 (2017.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 6/5223 (2013.01); A61B 34/10 (2016.02); G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G06T 7/155 (2017.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); A61B 2034/107 (2016.02); G06T 2207/10081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30096 (2013.01);
Abstract

A system and method for determining patient risk stratification is provided based on body composition derived from computed tomography images using segmentation with machine learning. The system may enable real-time segmentation for facilitating clinical application of body morphological analysis sets. A fully-automated deep learning system may be used for the segmentation of skeletal muscle cross sectional area (CSA). Whole-body volumetric analysis may also be performed. The fully-automated deep segmentation model may be derived from an extended implementation of a Fully Convolutional Network with weight initialization of a pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis.


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