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:
Dec. 15, 2020

Filed:

Jun. 02, 2018
Applicant:

Shenzhen Keya Medical Technology Corporation, Shenzhen, CN;

Inventors:

Qi Song, Seattle, WA (US);

Shanhui Sun, Princeton, NJ (US);

Hanbo Chen, Seattle, WA (US);

Junjie Bai, Seattle, WA (US);

Feng Gao, Seattle, WA (US);

Youbing Yin, Kenmore, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06N 3/04 (2006.01); G06N 5/04 (2006.01); G16H 30/40 (2018.01); G06T 11/20 (2006.01); G06N 20/00 (2019.01); G06T 7/73 (2017.01); G06N 3/08 (2006.01); G16H 50/70 (2018.01); G06K 9/32 (2006.01); A61B 5/00 (2006.01); G06N 3/063 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0037 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); G06K 9/3233 (2013.01); G06K 9/3241 (2013.01); G06N 3/04 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 7/73 (2017.01); G06T 11/20 (2013.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01); G06K 2209/053 (2013.01); G06K 2209/21 (2013.01); G06N 3/063 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30064 (2013.01); G06T 2210/12 (2013.01);
Abstract

A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.


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