US 12,118,724 B2
Interactive coronary labeling using interventional x-ray images and deep learning
Roy Franciscus Petrus Van Pelt, Tilburg (NL); and Javier Olivan Bescos, Eindhoven (NL)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed on Nov. 14, 2023, as Appl. No. 18/508,319.
Application 18/508,319 is a continuation of application No. 17/052,379, granted, now 11,861,825, previously published as PCT/EP2019/060430, filed on Apr. 24, 2019.
Claims priority of application No. 18170529 (EP), filed on May 3, 2018.
Prior Publication US 2024/0078676 A1, Mar. 7, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06T 11/60 (2006.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01)
CPC G06T 7/0012 (2013.01) [G06T 11/60 (2013.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30101 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of generating a model to classify a vasculature, the method comprising:
obtaining a trained model of a vasculature, wherein the trained model was trained from image data that represents a plurality of first vessel trees of a plurality of vasculatures and includes labeling of at least one vessel in each of the plurality of first vessel trees;
obtaining an image representing a second vessel tree;
identifying a deviation between a vessel geometry in the trained model and a vessel geometry in the second vessel tree;
providing a labeling of the deviation; and
adjusting, based on the deviation and the labeling of the deviation, the trained model to classify the vasculature.