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:
Aug. 25, 2026

Filed:

Apr. 01, 2022
Applicants:

Institute of Automation, Chinese Academy of Sciences, Beijing, CN;

University of Chinese Academy of Sciences, Beijing, CN;

Spermcatcher (Beijing) Biotechnology Co., Ltd, Beijing, CN;

Inventors:

Guole Liu, Beijing, CN;

Jie Jin, Beijing, CN;

Hao Shi, Beijing, CN;

Yuqiang Jiang, Beijing, CN;

Ge Yang, Beijing, CN;

Tao Yang, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06V 20/69 (2022.01); G16B 20/00 (2019.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 20/695 (2022.01); G06T 7/0012 (2013.01); G06V 20/698 (2022.01); G16B 20/00 (2019.02); G06T 2207/20036 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30168 (2013.01); G06V 10/82 (2022.01); G06V 2201/03 (2022.01);
Abstract

A deep neural network-based method for detecting living cell morphology may include identifying and locating one or more living cells within an acquired image to be detected by using a deep neural network-based target detection model, so as to extract one or more living single cell images. segmenting the image of the one or more living single cells by using a deep neural network-based cell segmentation model, so as to obtain one or more feature part of the one or more living single cells. and analyzing and determining a morphological parameter of the one or more living single cells based on the one or more feature parts. Thus, the activity of the detected cells can be ensured, and a non-destructive, accurate, and rapid detection of living cell morphology can be achieved.


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