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:
Apr. 14, 2026

Filed:

Aug. 22, 2023
Applicant:

GE Precision Healthcare Llc, Waukesha, WI (US);

Inventors:

Deepa Anand, Bengaluru, IN;

Bipul Das, Bengaluru, IN;

Vanika Singhal, Bengaluru, IN;

Rakesh Mullick, Bengaluru, IN;

Sandeep Dutta, Waukesha, WI (US);

Amy L Deubig, Waukesha, WI (US);

Maud Bonnard, Waukesha, WI (US);

Christine Smith, Waukesha, WI (US);

Assignee:

GE PRECISION HEALTHCARE LLC, Waukesha, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06T 7/60 (2017.01); G06T 15/00 (2011.01); G06V 10/24 (2022.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 7/60 (2013.01); G06T 15/00 (2013.01); G06V 10/24 (2022.01); G06V 10/44 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06V 2201/03 (2022.01);
Abstract

The current disclosure provides systems and methods for automatic image alignment of three-dimensional (3D) medical image volumes. The method includes pre-processing the 3D medical image volume by selecting a sub-volume of interest, detecting anatomical landmarks in the sub-volume using a deep neural network, estimating transformation parameters based on the anatomical landmarks to adjust rotation angles and translation of the sub-volume, adjusting the rotation angles and translation to produce a first aligned sub-volume, determining confidence in the transformation parameters based on the first aligned sub-volume, and iteratively refining the transformation parameters if the confidence is below a predetermined threshold. The disclosed approach for automated image alignment reduces the need for manual alignment and, increases a probability of the 3D image volume converging to a desired orientation compared to conventional approaches.


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