-
公开(公告)号：US11557036B2
公开(公告)日：2023-01-17
申请号：US16861353
申请日：2020-04-29
发明人： Rui Liao , Shun Miao , Pierre de Tournemire , Julian Krebs , Li Zhang , Bogdan Georgescu , Sasa Grbic , Florin Cristian Ghesu , Vivek Kumar Singh , Daguang Xu , Tommaso Mansi , Ali Kamen , Dorin Comaniciu
摘要： Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
-
公开(公告)号：US10282638B2
公开(公告)日：2019-05-07
申请号：US15217014
申请日：2016-07-22
发明人： Shanhui Sun , Tobias Heimann , Shun Miao , Rui Liao , Terrence Chen
摘要： A probe pose is detected in fluoroscopy medical imaging. The pose of the probe through a sequence of fluoroscopic images is detected. The detection relies on an inference framework for visual tracking overtime. By applying visual tracking, the pose through the sequence is consistent or the pose at one time guides the detection of the probe at another time. Single frame drop-out of detection may be avoided. Verification using detection of the tip of the probe and/or weighting of possible detections by separate detection of markers on the probe may further improve the accuracy.
-
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.
-
公开(公告)号：US20170337682A1
公开(公告)日：2017-11-23
申请号：US15587094
申请日：2017-05-04
发明人： Rui Liao , Shun Miao , Pierre de Tournemire , Julian Krebs , Li Zhang , Bogdan Georgescu , Sasa Grbic , Florin Cristian Ghesu , Vivek Kumar Singh , Daguang Xu , Tommaso Mansi , Ali Kamen , Dorin Comaniciu
CPC分类号： G06T7/0012 , A61B5/7267 , G06T7/30 , G06T2207/20081
摘要： Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
-
公开(公告)号：US09760690B1
公开(公告)日：2017-09-12
申请号：US15191043
申请日：2016-06-23
发明人： Kaloian Petkov , Shun Miao , Daphne Yu , Bogdan Georgescu , Klaus Engel , Tommaso Mansi , Dorin Comaniciu
CPC分类号： G06F19/345 , G06F19/00 , G06F19/321 , G06K9/6273 , G06K2209/05 , G06N3/0454 , G06T15/005 , G06T15/06 , G06T15/08 , G06T15/506 , G06T2210/41 , G16H50/20
摘要： An artificial intelligence agent is machine trained and used to provide physically-based rendering settings. By using deep learning and/or other machine training, settings of multiple rendering parameters may be provided for consistent imaging even in physically-based rendering.
-
公开(公告)号：US20230368383A1
公开(公告)日：2023-11-16
申请号：US18351548
申请日：2023-07-13
发明人： Rui Liao , Shun Miao , Pierre de Tournemire , Julian Krebs , Li Zhang , Bogdan Georgescu , Sasa Grbic , Florin Cristian Ghesu , Vivek Kumar Singh , Daguang Xu , Tommaso Mansi , Ali Kamen , Dorin Comaniciu
CPC分类号： G06T7/0012 , G06T7/30 , A61B5/7267 , G06T2207/20081
摘要： Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
-
公开(公告)号：US11132792B2
公开(公告)日：2021-09-28
申请号：US16271130
申请日：2019-02-08
发明人： Yue Zhang , Shun Miao , Rui Liao , Tommaso Mansi , Zengming Shen
摘要： Systems and method are described for medical image segmentation. A medical image of a patient in a first domain is received. The medical image comprises one or more anatomical structures. A synthesized image in a second domain is generated from the medical image of the patient in the first domain using a generator of a task driven generative adversarial network. The one or more anatomical structures are segmented from the synthesized image in the second domain using a dense image-to-image network of the task driven generative adversarial network. Results of the segmenting of the one or more anatomical structures from the synthesized image in the second domain represent a segmentation of the one or more anatomical structures in the medical image of the patient in the first domain.
-
公开(公告)号：US20200258227A1
公开(公告)日：2020-08-13
申请号：US16861353
申请日：2020-04-29
发明人： Rui Liao , Shun Miao , Pierre de Tournemire , Julian Krebs , Li Zhang , Bogdan Georgescu , Sasa Grbic , Florin Cristian Ghesu , Vivek Kumar Singh , Daguang Xu , Tommaso Mansi , Ali Kamen , Dorin Comaniciu
摘要： Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
-
公开(公告)号：US10607393B2
公开(公告)日：2020-03-31
申请号：US15827263
申请日：2018-01-03
发明人： Kaloian Petkov , Chen Liu , Shun Miao , Sandra Sudarsky , Daphne Yu , Tommaso Mansi
IPC分类号： G06T15/08 , G16H30/20 , G06T7/00 , G16H30/40 , A61B5/055 , A61B6/03 , A61B8/08 , G06K9/62 , G06T11/00 , G06T15/06 , G06T15/50 , G06N3/08
摘要： For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.
-
公开(公告)号：US20190205766A1
公开(公告)日：2019-07-04
申请号：US16233174
申请日：2018-12-27
发明人： Julian Krebs , Herve Delingette , Nicholas Ayache , Tommaso Mansi , Shun Miao
CPC分类号： G06N3/088 , G06K9/6256 , G06N3/0454 , G06N20/20 , G06T7/0012 , G06T2207/20081 , G06T2207/20084
摘要： For registration of medical images with deep learning, a neural network is designed to include a diffeomorphic layer in the architecture. The network may be trained using supervised or unsupervised approaches. By enforcing the diffeomorphic characteristic in the architecture of the network, the training of the network and application of the learned network may provide for more regularized and realistic registration.
-
-
-
-
-
-
-
-
-
搜索结果
42 条记录 (耗时 0.02 秒)
