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. 02, 2021

Filed:

Jan. 09, 2019
Applicant:

Shenzhen Imsight Medical Technology Co. Ltd., Guangdong, CN;

Inventors:

Qi Dou, Guangdong, CN;

Quande Liu, Guangdong, CN;

Hao Chen, Guangdong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/143 (2017.01); G06T 7/11 (2017.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0016 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); A61B 6/5205 (2013.01); G06K 9/6262 (2013.01); G06K 9/6277 (2013.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 2207/10081 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30064 (2013.01);
Abstract

Disclosed are a method and a device for detecting pulmonary nodule in Computed Tomography (CT) image, as well as a computer-readable storage medium. The method for detecting pulmonary nodule in CT image includes: obtaining a CT image to be detected, performing a pixel segmentation processing on the CT image through a pre-stored three-dimensional convolutional neural pixel segmentation network, to obtain a probability graph corresponding to the CT image, and obtaining a candidate nodule region by marking a connected domain on the probability graph; and predicting the candidate nodule region by various pre-stored prediction models corresponding to different three-dimensional convolutional neural network classifiers, to obtain various probability prediction values of the candidate nodule region, and comprehensively processing the various probability prediction values to obtain a classification result of the candidate nodule region.


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