Dilated fully convolutional network for multi-agent 2D/3D medical image registration
US10818019B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 14, 2018 |
| Grant date | Oct 27, 2020 |
| Priority date | — |
| Expiry date | Dec 5, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2219/2016
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment. The 3D medical volume is registered to the 2D medical image using final transformation parameters resulting from a plurality of iterations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.