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公开(公告)号：US20190050999A1
公开(公告)日：2019-02-14
申请号：US16103196
申请日：2018-08-14
发明人： Sébastien Piat , Shun Miao , Rui Liao , Tommaso Mansi , Jiannan Zheng
CPC分类号： G06T7/33 , G06K9/3233 , G06K9/6232 , G06T7/337 , G06T15/08 , G06T19/20 , G06T2207/10072 , G06T2207/10124 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2219/2004 , G06T2219/2016
摘要： 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.
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公开(公告)号：US11354813B2
公开(公告)日：2022-06-07
申请号：US17030955
申请日：2020-09-24
发明人： Sébastien Piat , Shun Miao , Rui Liao , Tommaso Mansi , Jiannan Zheng
摘要： 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.
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公开(公告)号：US20210012514A1
公开(公告)日：2021-01-14
申请号：US17030955
申请日：2020-09-24
发明人： Sébastien Piat , Shun Miao , Rui Liao , Tommaso Mansi , Jiannan Zheng
摘要： 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.
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