US 12,499,653 B2
Medical image analysis using machine learning and an anatomical vector
Stefan Vilsmeier, Munich (DE); and Jens Schmaler, Mering (DE)
Assigned to BRAINLAB AG, Munich (DE)
Appl. No. 17/783,851
Filed by Brainlab AG, Munich (DE)
PCT Filed Dec. 16, 2020, PCT No. PCT/EP2020/086421
§ 371(c)(1), (2) Date Jun. 9, 2022,
PCT Pub. No. WO2021/122747, PCT Pub. Date Jun. 24, 2021.
Prior Publication US 2023/0046321 A1, Feb. 16, 2023
Int. Cl. G06F 17/00 (2019.01); G06T 7/00 (2017.01); G06T 7/136 (2017.01); G06T 7/70 (2017.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/764 (2022.01) [G06T 7/0014 (2013.01); G06T 7/136 (2017.01); G06T 7/70 (2017.01); G06V 10/26 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06V 20/70 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/10061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06V 2201/03 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method of training a learning algorithm for determining a relation between a label for indicating a position or type of an anatomical structure in a medical image and the position or type of the anatomical structure in the medical image comprising:
acquiring patient training image data which describes digital medical images of an anatomical structure of a plurality of patients;
acquiring atlas data which describes an anatomical model of an anatomical body part including the anatomical structure;
acquiring viewing direction data which describes the viewing direction of an imaging device towards the anatomical structure at the point in time when the imaging device was used to generate the medical image;
determining anatomical vector data based on the viewing direction data and the atlas data, wherein the anatomical vector data describes an anatomical vector which is a result of transforming the viewing direction into a reference system in which positions in the anatomical model are defined;
acquiring label data which describes a label describing the position or type of the anatomical structure in the anatomical model; and
determining anatomical indicator data based on the patient training image data and the anatomical vector data and the label data, wherein the anatomical indicator data describes model parameters of a learning algorithm for establishing the relation between the position or type of the anatomical structure described by the medical image and the label, wherein the anatomical indicator data is determined by inputting the patient training image data and the label data into a function which establishes the relation.