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. 10, 2024

Filed:

Apr. 12, 2019
Applicant:

Koninklijke Philips N.v., Eindhoven, NL;

Inventors:

Ravindra Balasaheb Patil, Bangalore, IN;

Rithesh Sreenivasan, Bangalore, IN;

Krishnamoorthy Palanisamy, Bangalore, IN;

Nagaraju Bussa, Bangalore, IN;

Assignee:

KONINKLIJKE PHILIPS N.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 1/00 (2006.01); A61B 5/055 (2006.01); A61B 6/00 (2024.01); A61B 6/03 (2006.01); A61B 8/08 (2006.01); G06F 18/2431 (2023.01); G06N 3/08 (2023.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 1/000096 (2022.02); A61B 5/055 (2013.01); A61B 6/5223 (2013.01); A61B 8/523 (2013.01); G06F 18/2431 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); G06T 2207/10068 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 2201/03 (2022.01);
Abstract

A medical imaging system () includes a processor and memory with instructions executable by the processor to receive () three-dimensional medical image data () comprising multiple slices, receive () an imaging modality () of the three-dimensional medical image data, receive () an anatomical view classification () of the three-dimensional medical image data, select () a chosen abnormality detection module () from a set of abnormality detection modules () using the imaging modality and the anatomical view classification, wherein at least a portion of the abnormality detection modules is a convolution neural network trained for identifying if the at least a portion of the multiple slices as either normal or abnormal, classify () the at least a portion of the multiple slices as normal or abnormal using the abnormality detection module, and choose () a set of selected slices () from the multiple slices according to a predetermined selection criteria () if a predetermined number of the multiple slices are classified as abnormal.


Find Patent Forward Citations

Loading…