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. 31, 2023

Filed:

Nov. 04, 2020
Applicant:

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

Inventors:

Adam P Harrison, Bethesda, MD (US);

Ashwin Raju, Bethesda, MD (US);

Yuankai Huo, Bethesda, MD (US);

Jinzheng Cai, Bethesda, MD (US);

Le Lu, Bethesda, MD (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06K 9/62 (2022.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G06V 10/46 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6259 (2013.01); G06K 9/6265 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06V 10/462 (2022.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30056 (2013.01); G06V 2201/031 (2022.01);
Abstract

The present disclosure describes a computer-implemented method for processing clinical three-dimensional image. The method includes training a fully supervised segmentation model using a labelled image dataset containing images for a disease at a predefined set of contrast phases or modalities, allow the segmentation model to segment images at the predefined set of contrast phases or modalities; finetuning the fully supervised segmentation model through co-heterogenous training and adversarial domain adaptation (ADA) using an unlabelled image dataset containing clinical multi-phase or multi-modality image data, to allow the segmentation model to segment images at contrast phases or modalities other than the predefined set of contrast phases or modalities; and further finetuning the fully supervised segmentation model using domain-specific pseudo labelling to identify pathological regions missed by the segmentation model.


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