US 11,810,302 B2
Automated organ risk segmentation machine learning methods and systems
Pal Tegzes, Budapest (HU); Attila Radics, Budapest (HU); Eszter Csernai, Budapest (HU); and Laszlo Rusko, Szeged (HU)
Assigned to General Electric Company, Schenectady, NY (US)
Filed by General Electric Company, Schenectady, NY (US)
Filed on Nov. 2, 2020, as Appl. No. 17/087,240.
Application 17/087,240 is a continuation of application No. 15/958,546, filed on Apr. 20, 2018, granted, now 10,825,168.
Claims priority of provisional application 62/488,442, filed on Apr. 21, 2017.
Prior Publication US 2021/0073987 A1, Mar. 11, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/11 (2017.01); G06T 7/00 (2017.01)
CPC G06T 7/11 (2017.01) [G06T 7/0012 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An image data processing system comprising a processor to:
detect an anatomy in an image and to remove items not included in the anatomy from the image;
generate a bounding box around a region of interest in the anatomy;
classify image data within the bounding box at the voxel level to identify an object in the region of interest; and
output an indication of the object identified in the region of interest segmented in the image, wherein the indication is generated based on both a first fully connected neural network and a gradient boosting machine, wherein the first fully connected neural network and the gradient boosting machine are built using a plurality of image features created using image intensity values and at least one additional neural network, wherein the indication further comprises radiation planning information comprising radiation location information and radiation dosage information for the object, and wherein the object is an organ identified from the image.