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公开(公告)号：US10957037B2
公开(公告)日：2021-03-23
申请号：US15697559
申请日：2017-09-07
发明人： Rui Liao , Erin Girard , Shun Miao , Xianjun S. Zheng
摘要： Systems and methods are provided for determining a set of imaging parameters for an imaging system. A selection of an image is received from a set of images. A modification of certain quality measures is received for the selected image. The modified selected image is mapped to a set of imaging parameters of an imaging system based on the certain quality measures using a trained Deep Reinforcement Learning (DRL) agent.
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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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公开(公告)号：US20190259153A1
公开(公告)日：2019-08-22
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.
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公开(公告)号：US20190057505A1
公开(公告)日：2019-02-21
申请号：US16028787
申请日：2018-07-06
发明人： Thomas Pheiffer , Shun Miao , Rui Liao , Pavlo Dyban , Michael Suehling , Tommaso Mansi
CPC分类号： G06T7/0016 , A61B5/0033 , A61B5/055 , G06T7/136 , G06T2200/04 , G06T2207/10081 , G06T2207/20021 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/20224 , G06T2207/30004 , G06T2207/30061
摘要： Systems and methods are provided for identifying pathological changes in follow up medical images. Reference image data is acquired. Follow up image data is acquired. A deformation field is generated for the reference image data and the follow up data using a machine-learned network trained to generate deformation fields describing healthy, anatomical deformation between input reference image data and input follow up image data. The reference image data and the follow up image data are aligned using the deformation field. The co-aligned reference image data and follow up image data are analyzed for changes due to pathological phenomena.
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公开(公告)号：US20180130200A1
公开(公告)日：2018-05-10
申请号：US15344210
申请日：2016-11-04
发明人： Shun Miao , Rui Liao , Ryan Spilker
CPC分类号： G06T7/0012 , A61B6/12 , A61B6/5223 , A61B8/12 , A61B8/4245 , G06T7/74 , G06T2207/10028 , G06T2207/10116 , G06T2207/30021 , G06T2207/30244
摘要： Pose of a probe is detected in x-ray medical imaging. Since the TEE probe is inserted through the esophagus of a patient, the pose is limited to being within the esophagus. The path of the esophagus is determined from medical imaging prior to the intervention. During the intervention, the location in 2D is found from one x-ray image at a given time. The 3D probe location is provided by assigning the depth of the esophagus at that 2D location to be the depth of the probe. A single x-ray image may be used to determine the probe location in 3D, allowing for real-time pose determination without requiring space to rotate a C-arm during the intervention.
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公开(公告)号：US20240177458A1
公开(公告)日：2024-05-30
申请号：US18058884
申请日：2022-11-28
IPC分类号： G06V10/774 , G06T7/00 , G06V10/26
CPC分类号： G06V10/774 , G06T7/0012 , G06V10/26 , G06T2207/30168 , G06V2201/03
摘要： Systems and methods for training a machine learning based segmentation network are provided. A set of medical images, each depicting an anatomical object, in a first modality is received. For each respective medical image of the set of medical images, a synthetic image, depicting the anatomical object, in a second modality is generated based on the respective medical image. One or more augmented images are generated based on the synthetic image. One or more segmentations of the anatomical object are performed from the one or more augmented images using a machine learning based reference network. An uncertainty associated with segmenting the anatomical object from the respective medical image is computed based on results of the one or more segmentations. It is determined whether the respective medical image is suitable for training a machine learning based segmentation network based on the uncertainty. The machine learning based segmentation network is trained based on 1) the suitable medical images of the set of medical images and 2) annotations of the anatomical object determined using a machine learning based teacher network.
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公开(公告)号：US20230316544A1
公开(公告)日：2023-10-05
申请号：US17657366
申请日：2022-03-31
发明人： Abdoul Aziz Amadou , Rui Liao , Yue Zhang
CPC分类号： G06T7/248 , G06T7/74 , G06T2207/30004 , G06T2207/20084 , G06T2207/10016 , G06T2207/20081
摘要： Systems and methods for tracking a location of an object of interest through a sequence of medical images are provided. First and second input medical images of a patient are received. The first input medical image comprises an annotation of a location of an object of interest. Features are extracted from the first and the second input medical images. A location of the object of interest in the second input medical image is determined using a machine learning based location predictor network based on the annotation of the location of the object of interest in the first input medical image and the extracted features from the first and the second input medical images. The location of the object of interest in the second input medical image is output. The machine learning based location predictor network is trained based on a comparison between 1) locations of a particular object in a sequence of training images determined during a forward tracking of the particular object through the sequence of training images and 2) locations of the particular object determined during a backward tracking of the particular object through the sequence of training images.
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公开(公告)号：US11741605B2
公开(公告)日：2023-08-29
申请号：US18064366
申请日：2022-12-12
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.
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公开(公告)号：US20230165638A1
公开(公告)日：2023-06-01
申请号：US17456681
申请日：2021-11-29
发明人： Tommaso Mansi , Young-Ho Kim , Rui Liao , Yue Zhang , Puneet Sharma , Dorin Comaniciu
CPC分类号： A61B34/20 , G06T7/11 , G06T2207/20081 , G06T2207/30101 , A61B2034/2065
摘要： Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions. The catheter is navigated from the current position towards the target position using the robotic navigation system according to the one or more actions based on the evaluation.
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公开(公告)号：US10929989B2
公开(公告)日：2021-02-23
申请号：US16173853
申请日：2018-10-29
发明人： Tanja Kurzendorfer , Rui Liao , Tommaso Mansi , Shun Miao , Peter Mountney , Daniel Toth
摘要： The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.
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