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:
Feb. 04, 2020

Filed:

Aug. 29, 2018
Applicant:

Beijing Curacloud Technology Co., Ltd., Beijing, CN;

Inventors:

Dan Wu, Bellevue, WA (US);

Xin Wang, Seattle, WA (US);

Youbing Yin, Kenmore, WA (US);

Yuwei Li, Bellevue, WA (US);

Kunlin Cao, Kenmore, WA (US);

Qi Song, Seattle, WA (US);

Bin Ouyang, Shenzhen, CN;

Shuyi Liang, Shaoguan, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); A61B 6/00 (2006.01); G16H 30/40 (2018.01); A61B 5/00 (2006.01); G06T 7/10 (2017.01); A61B 5/02 (2006.01); G06T 19/00 (2011.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
A61B 6/504 (2013.01); A61B 5/0073 (2013.01); A61B 5/02007 (2013.01); G06T 7/10 (2017.01); G16H 30/40 (2018.01); G06T 19/00 (2013.01); G06T 2207/30101 (2013.01); G16H 50/50 (2018.01);
Abstract

The present disclosure is directed to a method and device for generating anatomical labels for a physiological tree structure. The method may include receiving a 3D model and a 3D skeleton line of the physiological tree structure. The 3D model is restructured based on medical image data of the physiological tree structure acquired by an imaging device. The method further includes selecting at least one level from extracting geometrical features from a pool of selectable levels. The method also includes extracting, by a processor, geometrical features from the 3D model of the physiological tree structure along the 3D skeleton line at the selected at least one level. The method also includes generating, by the processor, anatomical labels for the physiological tree structure using a trained learning network based on the extracted geometrical features.


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