Patent · US Active

Training a machine learning algorithm using digitally reconstructed radiographs

US12080021B2 · kind B2 · utility

0Cited by

10References

13Claims

0Family size

Assignee

Brainlab AG · DE

Inventors

Kim Le · Munich, DE
Yannic Meurer · Laim, DE
Thomas Drexl · Königsbrunn, DE
Thomas Feilkas · Grafing, DE

Key dates

Filing date	Sep 20, 2019
Grant date	Sep 3, 2024
Priority date	—
Expiry date	Oct 7, 2041

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30004
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Disclosed is a computer-implemented method of training a likelihood-based computational model for determining the position of an image representation of an annotated anatomical structure in a two-dimensional x-ray image, wherein the method encompasses inputting medical DRRs together with annotation to a machine learning algorithm to train the algorithm, i.e. to generate adapted leamable parameters of the machine learning model. The annotations may be derived from metadata associated with the DRRs or may be included in atlas data which is matched with the DRRs to establish a relation between the annotations included in the atlas data and the DRRs. The thus generated machine learning algorithm may then be used to analyse clinical or synthesized DRRs so as to appropriately add annotations to those DRRs and/or identify the position of an anatomical structure in those DRRs.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.