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:
Apr. 12, 2022

Filed:

Jun. 24, 2020
Applicants:

Samsung Life Public Welfare Foundation, Seoul, KR;

Seoul National University R&db Foundation, Seoul, KR;

Inventors:

Heejung Kim, Seoul, KR;

Ji Min Lee, Daejeon, KR;

Hyung Joo Cho, Seoul, KR;

Sung Joon Ye, Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06V 10/44 (2022.01); G16H 50/20 (2018.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06N 3/04 (2006.01); G06K 9/62 (2022.01); A61N 5/10 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06V 10/44 (2022.01); A61N 5/1039 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06V 2201/031 (2022.01);
Abstract

The present disclosure relates to a system and method for segmenting a normal organ and/or tumor structure based on artificial intelligence for radiation treatment planning. The system may include a data collection unit configured to collect a radiotherapy structure (RT-structure) file including a computerized tomography (CT) image of a patient and contour information of an area of interest for radiation treatment, a pre-processing unit configured to extract the contour information from the RT-structure file and generate a binary image based on the extracted contour information, and a model training unit configured to learn parameters for generating a segmentation map indicative of the area of interest using a deep learning algorithm, based on the binary image, and generate a trained model based on the learnt parameter.


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