Invention Grant
- Patent Title: Dilated fully convolutional network for 2D/3D medical image registration
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Application No.: US17030955Application Date: 2020-09-24
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Publication No.: US11354813B2Publication Date: 2022-06-07
- Inventor: Sébastien Piat , Shun Miao , Rui Liao , Tommaso Mansi , Jiannan Zheng
- Applicant: Siemens Healthcare GmbH
- Applicant Address: DE Erlangen
- Assignee: Siemens Healthcare GmbH
- Current Assignee: Siemens Healthcare GmbH
- Current Assignee Address: DE Erlangen
- Main IPC: G06T7/33
- IPC: G06T7/33 ; G06T15/08 ; G06T19/20 ; G06K9/62 ; G06V10/25

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.
Public/Granted literature
- US20210012514A1 Dilated Fully Convolutional Network for 2D/3D Medical Image Registration Public/Granted day:2021-01-14
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