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:
Oct. 13, 2020

Filed:

Aug. 07, 2018
Applicant:

Shenzhen Keya Medical Technology Corporation, Shenzhen, CN;

Inventors:

Xin Wang, Seattle, WA (US);

Youbing Yin, Kenmore, WA (US);

Kunlin Cao, Kenmore, WA (US);

Yuwei Li, Bellevue, WA (US);

Junjie Bai, Seattle, WA (US);

Xiaoyang Xu, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 6/00 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06T 17/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); A61B 6/504 (2013.01); A61B 6/507 (2013.01); A61B 6/5217 (2013.01); G06K 9/4604 (2013.01); G06K 9/6257 (2013.01); G06T 17/00 (2013.01); G06K 2209/05 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30104 (2013.01);
Abstract

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.


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