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:
Sep. 19, 2023

Filed:

Apr. 30, 2021
Applicant:

Acist Medical Systems, Inc., Eden Prairie, MN (US);

Inventors:

Jonathan Tung, Cupertino, CA (US);

Jung W Suh, Palo Alto, CA (US);

Advit Bhatt, Danville, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/12 (2017.01); G06F 18/23 (2023.01); G06V 10/75 (2022.01); G06V 10/762 (2022.01); G06V 10/80 (2022.01); G06V 10/28 (2022.01);
U.S. Cl.
CPC ...
G06T 7/12 (2017.01); G06F 18/23 (2023.01); G06V 10/28 (2022.01); G06V 10/752 (2022.01); G06V 10/763 (2022.01); G06V 10/809 (2022.01); G06T 2207/10132 (2013.01); G06T 2207/30101 (2013.01);
Abstract

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.


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