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. 05, 2017

Filed:

Jul. 31, 2015
Applicant:

California Institute of Technology, Pasadena, CA (US);

Inventors:

Babak Kateb, Brentwood, CA (US);

Shouleh Nikzad, Valencia, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); G02B 21/36 (2006.01); G02B 21/00 (2006.01); G02B 23/24 (2006.01); A61B 6/00 (2006.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); G01R 33/48 (2006.01); A61B 8/08 (2006.01); A61B 90/00 (2016.01); A61B 34/10 (2016.01); G01R 33/56 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); A61B 5/0035 (2013.01); A61B 5/0042 (2013.01); A61B 5/7267 (2013.01); A61B 6/5247 (2013.01); A61B 90/37 (2016.02); G02B 21/0012 (2013.01); G02B 21/367 (2013.01); G02B 23/24 (2013.01); G06K 9/0014 (2013.01); A61B 5/0084 (2013.01); A61B 5/055 (2013.01); A61B 5/4064 (2013.01); A61B 5/4094 (2013.01); A61B 5/7203 (2013.01); A61B 8/5261 (2013.01); A61B 34/10 (2016.02); A61B 2090/364 (2016.02); G01R 33/4808 (2013.01); G01R 33/5608 (2013.01); G06K 9/00147 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10092 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/30016 (2013.01);
Abstract

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.


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