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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.

Patent No.:

US 11701066 B1

PDFFull Text
Date of Patent:
Jul. 18, 2023

Filed:

Nov. 11, 2020

Device and method for detecting clinically important objects in medical images with distance-based decision stratification

Applicant:

Ping an Technology (Shenzhen) Co., Ltd., Shenzhen, CN;

Inventors:

Ke P Yan, Bethesda, MD (US);

Zhuotun Zhu, Bethesda, MD (US);

Dakai Jin, Bethesda, MD (US);

Jinzheng Cai, Bethesda, MD (US);

Adam P Harrison, Bethesda, MD (US);

Dazhou Guo, Bethesda, MD (US);

Le Lu, Bethesda, MD (US);

Assignee:

PING AN TECHNOLOGY (SHENZHEN) CO., LTD., Shenzhen, CN;

Attorney:

Anova Law Group, PLLC

Primary Examiner:

Vu Le

Assistant Examiner:

Julius Chai

Int. Cl.
CPC ...
A61B 6/03 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06T 9/00 (2006.01); A61B 6/00 (2006.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 7/70 (2017.01); G06F 18/21 (2023.01); G06F 18/25 (2023.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01);
U.S. Cl.
CPC ...
A61B 6/037 (2013.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 6/5235 (2013.01); G06F 18/21 (2023.01); G06F 18/251 (2023.01); G06N 3/08 (2013.01); G06T 7/0014 (2013.01); G06T 7/70 (2017.01); G06T 9/00 (2013.01); G06T 11/008 (2013.01); G06V 10/764 (2022.01); G06V 10/806 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01);
Abstract

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

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