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公开(公告)号：US12051204B2
公开(公告)日：2024-07-30
申请号：US17538232
申请日：2021-11-30
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
CPC classification number: G06T7/0014 , G06N3/08 , G06T7/50 , G06T7/74 , G16H30/40 , G16H50/50 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
Abstract: The shape and/or location of an organ may change in accordance with changes in the body shape and/or pose of a patient. Described herein are systems, methods, and instrumentalities for automatically determining, using an artificial neural network (ANN), the shape and/or location of the organ based on human models that reflect the body shape and/or pose the patient. The ANN may be trained to learn the spatial relationship between the organ and the body shape or pose of the patient. Then, at an inference time, the ANN may be used to determine the relationship based on a first patient model and a first representation (e.g., a point cloud) of the organ so that given a second patient model thereafter, the ANN may automatically determine the shape and/or location of the organ corresponding to the body shape or pose of the patient indicated by the second patient model.
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公开(公告)号：US12045695B2
公开(公告)日：2024-07-23
申请号：US16804907
申请日：2020-02-28
Inventor： Srikrishna Karanam , Ziyan Wu , Abhishek Sharma , Arun Innanje , Terrence Chen
Abstract: Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.
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公开(公告)号：US20240135737A1
公开(公告)日：2024-04-25
申请号：US18128290
申请日：2023-03-29
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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公开(公告)号：US20240135684A1
公开(公告)日：2024-04-25
申请号：US17969876
申请日：2022-10-19
Inventor： Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
IPC: G06V10/774 , G06T7/00
CPC classification number: G06V10/774 , G06T7/0012 , G06T2207/20081 , G06T2207/20108
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D annotation provided by an annotator and by propagating the 2D annotation through multiple images of a sequence of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets based on similarities between the first 3D image dataset and the other 3D image datasets. The automatic annotation of the first 3D image dataset and/or the other 3D image datasets may be conducted based on one or more machine-learning models trained for performing those tasks.
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公开(公告)号：US11963741B2
公开(公告)日：2024-04-23
申请号：US18095857
申请日：2023-01-11
Inventor： Ziyan Wu , Srikrishna Karanam , Changjiang Cai , Georgios Georgakis
IPC: G06F18/21 , A61B5/00 , G06F18/214 , G06T7/00 , G06T7/50 , G06T7/70 , G06T7/90 , G06T17/00 , G06T17/20 , G06V10/40 , G06V10/42 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82 , G06V20/62 , G06V20/64 , G06V40/10 , G06V40/20 , G16H10/60 , G16H30/20 , G16H30/40
CPC classification number: A61B5/0077 , A61B5/0035 , A61B5/70 , G06F18/21 , G06F18/214 , G06F18/2193 , G06T7/0012 , G06T7/50 , G06T7/70 , G06T7/90 , G06T17/00 , G06T17/20 , G06V10/40 , G06V10/42 , G06V10/764 , G06V10/774 , G06V10/7796 , G06V10/82 , G06V20/62 , G06V20/64 , G06V40/10 , G06V40/20 , G16H10/60 , G16H30/20 , G16H30/40 , G06T2200/08 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2207/30196 , G06V2201/033
Abstract: The pose and shape of a human body may be recovered based on joint location information associated with the human body. The joint location information may be derived based on an image of the human body or from an output of a human motion capture system. The recovery of the pose and shape of the human body may be performed by a computer-implemented artificial neural network (ANN) trained to perform the recovery task using training datasets that include paired joint location information and human model parameters. The training of the ANN may be conducted in accordance with multiple constraints designed to improve the accuracy of the recovery and by artificially manipulating the training data so that the ANN can learn to recover the pose and shape of the human body even with partially observed joint locations.
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Patent Agency Ranking
公开(公告)号：US11937967B2
公开(公告)日：2024-03-26
申请号：US18149111
申请日：2023-01-01
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma , Ren Li
Abstract: Systems, methods and instrumentalities are described herein for automating a medical environment. The automation may be realized using one or more sensing devices and at least one processing device. The sensing devices may be configured to capture images of the medical environment and provide the images to the processing device. The processing device may determine characteristics of the medical environment based on the images and automate one or more aspects of the operations in the medical environment. These characteristics may include, e.g., people and/or objects present in the images and respective locations of the people and/or objects in the medical environment. The operations that may be automated may include, e.g., maneuvering and/or positioning a medical device based on the location of a patient, determining and/or adjusting the parameters of a medical device, managing a workflow, providing instructions and/or alerts to a patient or a physician, etc.
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37.发明公开公开(公告)号：US20240077562A1
公开(公告)日：2024-03-07
申请号：US17939251
申请日：2022-09-07
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Terrence Chen
CPC classification number: G01R33/5608 , A61B5/055 , A61N5/1049 , A61N2005/1055
Abstract: A power management apparatus for a workflow to enable low power MR patient positioning on edge devices is disclosed. The power management apparatus changes an operational mode of an edge device from a first power mode to a second power mode after a defined time-interval. The power management apparatus further controls the edge device to capture a first image of a first scene. The power management apparatus further determines a trigger point based on a detection of a plurality of objects in the captured first image. The power management apparatus further changes the operational mode of the edge device from the second power mode to a third power mode to control a consumption of electric power while a set of operations is executed at the edge device. The operational mode of the edge device may be changed at the determined trigger point.
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公开(公告)号：US20240070905A1
公开(公告)日：2024-02-29
申请号：US17897465
申请日：2022-08-29
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng , Abhishek Sharma
IPC: G06T7/73
CPC classification number: G06T7/74 , G06T2207/30196
Abstract: The 3D pose of a person may be estimated by triangulating 2D representations of body keypoints (e.g., joint locations) of the person. The triangulation may leverage various metrics such as confidence scores associated with the 2D representations of a keypoint and/or temporal consistency between multiple 3D representations of the keypoint. The 2D representations may be arranged into groups, a candidate 3D representation may be determined for each group, taking into account of the confidence score of each 2D representation in the group, and the candidate 3D representation that has the smallest error may be used to represent the keypoint. Other 3D representation(s) of the keypoint determined from images taken at different times may be used to refine the 3D representation of the keypoint.
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公开(公告)号：US11756240B2
公开(公告)日：2023-09-12
申请号：US16804985
申请日：2020-02-28
Inventor： Arun Innanje , Shanhui Sun , Abhishek Sharma , Zhang Chen , Ziyan Wu
CPC classification number: G06T11/005 , G06T1/20 , G06T19/20 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/30004
Abstract: A standalone image reconstruction device is configured to reconstruct the raw signals received from a radiology scanner device into a reconstructed output signal. The image reconstruction device is a vendor neutral interface between the radiology scanner device and the post processing imaging device. The reconstructed output signal is a user readable domain that can be used to generate a medical image or a three-dimensional (3D) volume. The apparatus is configured to reconstruct signals from different types of radiology scanner devices using any suitable image reconstruction protocol.
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公开(公告)号：US11710244B2
公开(公告)日：2023-07-25
申请号：US16673817
申请日：2019-11-04
Inventor： Shanhui Sun , Zhang Chen , Terrence Chen , Ziyan Wu
IPC: G06N3/08 , G06T7/246 , A61B5/11 , A61B5/107 , G06T7/20 , G06T7/62 , G06T7/215 , G06T7/00 , G06T11/00 , G06F18/2132 , G06F18/214 , G06F18/21 , G06V10/25 , G06V10/764 , G06V10/774 , A61B90/00
CPC classification number: G06T7/248 , A61B5/1076 , A61B5/1107 , A61B5/1128 , G06F18/2132 , G06F18/2155 , G06F18/2178 , G06N3/08 , G06T7/0016 , G06T7/20 , G06T7/215 , G06T7/251 , G06T7/62 , G06T11/005 , G06V10/25 , G06V10/764 , G06V10/7753 , A61B2090/061 , G06T2207/10088 , G06T2207/20081 , G06T2207/30048 , G06T2207/30061
Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.
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