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:
Dec. 31, 2024

Filed:

Dec. 08, 2022
Applicant:

Arterys Inc., San Francisco, CA (US);

Inventors:

Daniel Irving Golden, Palo Alto, CA (US);

Fabien Rafael David Beckers, San Francisco, CA (US);

John Axerio-Cilies, Berkeley, CA (US);

Matthieu Le, San Francisco, CA (US);

Jesse Lieman-Sifry, San Francisco, CA (US);

Anitha Priya Krishnan, Foster City, CA (US);

Sean Patrick Sall, Indianapolis, IN (US);

Hok Kan Lau, San Francisco, CA (US);

Matthew Joseph Didonato, Redwood City, CA (US);

Robert George Newton, Calgary, CA;

Torin Arni Taerum, Calgary, CA;

Shek Bun Law, Calgary, CA;

Carla Rosa Leibowitz, San Carlos, CA (US);

Angélique Sophie Calmon, Montauban, FR;

Assignee:

Arterys Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 10/82 (2022.01); G16H 10/60 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06V 10/82 (2022.01); G16H 10/60 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30056 (2013.01); G06T 2207/30064 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.


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