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:
Aug. 22, 2023

Filed:

Feb. 22, 2021
Applicant:

Board of Regents of the University of Texas System, Austin, TX (US);

Inventors:

Nitin Tandon, Houston, TX (US);

Mehmet Kadipasaoglu, Houston, TX (US);

Kevin Pham, Sugar Lang, TX (US);

Cristian Donos, Bucharest, RO;

Kiefer Forseth, Houston, TX (US);

Patrick Sarahan Rollo, Houston, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/37 (2017.01); G06T 7/11 (2017.01); G06T 7/38 (2017.01); G01R 33/56 (2006.01); A61B 34/10 (2016.01);
U.S. Cl.
CPC ...
G06T 7/37 (2017.01); A61B 34/10 (2016.02); G01R 33/5607 (2013.01); G06T 7/11 (2017.01); G06T 7/38 (2017.01); A61B 2034/102 (2016.02); A61B 2034/107 (2016.02); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/30016 (2013.01);
Abstract

A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.


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