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:
Jan. 11, 2022

Filed:

Feb. 11, 2020
Applicant:

Deepvoxel, Inc., Irvine, CA (US);

Inventors:

Hao Tang, Irvine, CA (US);

Yang Liu, Foster City, CA (US);

Shanlin Sun, Irvine, CA (US);

Narisu Bai, Riverside, CA (US);

Assignee:

Other;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/11 (2017.01); G06T 7/00 (2017.01); G06K 9/46 (2006.01); G16H 30/40 (2018.01); G06N 3/08 (2006.01); A61N 5/10 (2006.01); G16H 20/40 (2018.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); A61N 5/1039 (2013.01); G06K 9/46 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G16H 20/40 (2018.01); G16H 30/40 (2018.01); G06K 2209/051 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30061 (2013.01);
Abstract

A system and methods for automatically delineating OARs in whole-volume medical images through a two-stage DCNN model, the DCNN model comprising an OAR detection network and an OAR segmentation network, and the method comprising the steps of: inputting the whole-volume medical images to the OAR detection network; extracting image features through a sequence of downsampling blocks; generating a final detection feature map via upsampling and concatenation; detecting at least one OAR candidate by branching the final detection feature map, wherein the at least one OAR candidate is defined by a predicted bounding box with a class label; inputting the predicted bounding box and corresponding class label to the OAR segmentation network; cropping the final detection feature map and a downsampling block in the OAR detection network according to the predicted bounding box; concatenating the cropped feature maps and generating a predicted binary mask delineating OARs according to the class label.


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