US 12,462,405 B2
Automatic 3D medical image orientation determination
Leonid Vlasenkov, Moscow (RU)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 17/920,056
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Apr. 21, 2021, PCT No. PCT/EP2021/060285
§ 371(c)(1), (2) Date Oct. 20, 2022,
PCT Pub. No. WO2021/214089, PCT Pub. Date Oct. 28, 2021.
Claims priority of application No. RU2020114316 (RU), filed on Apr. 21, 2020.
Prior Publication US 2023/0169667 A1, Jun. 1, 2023
Int. Cl. G06T 7/32 (2017.01); G06T 7/11 (2017.01); G06T 7/33 (2017.01)
CPC G06T 7/32 (2017.01) [G06T 7/11 (2017.01); G06T 7/337 (2017.01); G06T 2207/10072 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30196 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving a set of medical images from a medical imager;
selecting one or more anchor organs from the medical images;
generating, based on applying a segmentation model to each of the set of medical images, a segmentation mask for the selected one or more anchor organs, wherein the segmentation model is trained to identify the selected one or more anchor organs in the set of medical images based on a training dataset received from a database;
computing image coordinates for the selected one or more anchor organs in each of the set of medical images based on a center of mass of each of the selected one or more anchor organs and the generated segmentation mask;
determining a correlation between the image coordinates for the selected one or more anchor organs in each of the set of medical images and corresponding anatomical coordinates for the selected one or more anchor organs in the training dataset; and
aligning each of the set of medical images based on the correlation between the image coordinates and the anatomical coordinates.