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:
Oct. 12, 2021

Filed:

Apr. 10, 2020
Applicant:

Agilent Technologies, Inc., Santa Clara, CA (US);

Inventors:

Elad Arbel, Tel-Aviv, IL;

Itay Remer, Tel-Aviv, IL;

Amir Ben-Dor, Kfar-Saba, IL;

Assignee:

Agilent Technologies, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/10 (2017.01); G06T 7/187 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06N 20/00 (2019.01); G06F 3/0482 (2013.01); G06F 3/0486 (2013.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 3/0482 (2013.01); G06F 3/0486 (2013.01); G06K 9/6202 (2013.01); G06K 9/628 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/0014 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 7/187 (2017.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20104 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation via multiple regression layers, implementing instance segmentation based on partial annotations, and/or implementing user interface configured to facilitate user annotation for instance segmentation. In various embodiments, a computing system might generate a user interface configured to collect training data for predicting instance segmentation within biological samples, and might display, within a display portion of the user interface, the first image comprising a field of view of a biological sample. The computing system might receive, from a user via the user interface, first user input indicating a centroid for each of a first plurality of objects of interest and second user input indicating a border around each of the first plurality of objects of interest. The computing system might train an AI system to predict instance segmentation of objects of interest in images of biological samples.


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