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:
Nov. 16, 2021

Filed:

Apr. 08, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Sun Y. Park, San Diego, CA (US);

Dustin Sargent, San Diego, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61B 8/08 (2006.01); A61B 90/00 (2016.01); G06T 7/00 (2017.01); G06T 7/30 (2017.01); G06K 9/34 (2006.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7264 (2013.01); A61B 5/055 (2013.01); A61B 5/4842 (2013.01); A61B 6/032 (2013.01); A61B 6/486 (2013.01); A61B 6/5217 (2013.01); A61B 6/5247 (2013.01); A61B 8/085 (2013.01); A61B 8/5223 (2013.01); A61B 8/5261 (2013.01); A61B 90/37 (2016.02); G06K 9/00 (2013.01); G06K 9/342 (2013.01); G06T 7/0016 (2013.01); G06T 7/30 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 5/004 (2013.01); A61B 5/7275 (2013.01); A61B 2090/364 (2016.02); A61B 2576/02 (2013.01); A61B 2576/026 (2013.01); G06K 9/6277 (2013.01); G06K 2209/05 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.


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