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公开(公告)号：US20230419507A1
公开(公告)日：2023-12-28
申请号：US17851494
申请日：2022-06-28
Inventor： Benjamin Planche , Liangchen Song , Ziyan Wu , Meng Zheng
CPC classification number: G06T7/246 , G06T7/90 , G06T15/20 , G06T2207/20081 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and/or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.
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公开(公告)号：US20230200767A1
公开(公告)日：2023-06-29
申请号：US17560492
申请日：2021-12-23
Inventor： Meng Zheng , Elena Zhao , Srikrishna Karanam , Ziyan Wu , Terrence Chen
CPC classification number: A61B6/468 , A61B6/469 , G06T7/149 , G06T2207/30204 , G06T2207/10081 , G06T2207/10088 , G06T2207/30096
Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.
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公开(公告)号：US20230148978A1
公开(公告)日：2023-05-18
申请号：US17525488
申请日：2021-11-12
Inventor： Meng Zheng , Abhishek Sharma , Srikrishna Karanam , Ziyan Wu
CPC classification number: A61B6/0407 , G06T7/70 , G06K9/6267 , G06N20/20 , G06T2207/20084
Abstract: Automated patient positioning and modelling includes a hardware processor to obtain image data from an imaging sensor, classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning, provide a confidence score based on the classification of the image data and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on one or more pre-defined protocols for patient positioning during a scan procedure.
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公开(公告)号：US20230140003A1
公开(公告)日：2023-05-04
申请号：US17513534
申请日：2021-10-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
Abstract: Systems, methods, and instrumentalities are described herein for constructing a multi-view patient model (e.g., a 3D human mesh model) based on multiple single-view models of the patient. Each of the single-view models may be generated based on images captured by a sensing device and, dependent on the field of the view of the sensing device, may depict some keypoints of the patient's body with a higher accuracy and other keypoints of the patient's body with a lower accuracy. The multi-view patient model may be constructed using respective portions of the single-view models that correspond to accurately depicted keypoints. This way, a comprehensive and accurate depiction of the patient's body shape and pose may be obtained via the multi-view model even if some keypoints of the patient's body are blocked from a specific sensing device.
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公开(公告)号：US20230032103A1
公开(公告)日：2023-02-02
申请号：US17966000
申请日：2022-10-14
Inventor： Srikrishna Karanam , Yimo Guo , Ziyan Wu
Abstract: Healthcare services can be automated utilizing a system that recognizes at least one characteristic of a patient based on images of the patient acquired by an image capturing device. Relying on information extracted from these images, the system may automate multiple aspects of a medical procedure such as patient identification and verification, positioning, diagnosis and/or treatment planning using artificial intelligence or machine learning techniques. By automating these operations, healthcare services can be provided remotely and/or with minimum physical contact between the patient and a medical professional.
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Patent Agency Ranking
公开(公告)号：US11475997B2
公开(公告)日：2022-10-18
申请号：US16798100
申请日：2020-02-21
Inventor： Srikrishna Karanam , Yimo Guo , Ziyan Wu
Abstract: Healthcare services can be automated utilizing a system that recognizes at least one characteristic of a patient based on images of the patient acquired by an image capturing device. Relying on information extracted from these images, the system may automate multiple aspects of a medical procedure such as patient identification and verification, positioning, diagnosis and/or treatment planning using artificial intelligence or machine learning techniques. By automating these operations, healthcare services can be provided remotely and/or with minimum physical contact between the patient and a medical professional.
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公开(公告)号：US20220277836A1
公开(公告)日：2022-09-01
申请号：US17737694
申请日：2022-05-05
Inventor： Ziyan Wu , Srikrishna Karanam , Arun Innanje , Shanhui Sun , Abhishek Sharma , Yimo Guo , Zhang Chen
IPC: G16H30/40 , G06T7/00 , G06T7/90 , G06T17/00 , G06T7/50 , G06T7/70 , G06K9/62 , G06T17/20 , G16H10/60 , G16H30/20 , A61B5/00 , G06V10/40 , G06V10/42 , G06V20/64 , G06V40/10 , G06V40/20 , G06V20/62
Abstract: A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.
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公开(公告)号：US11379727B2
公开(公告)日：2022-07-05
申请号：US16694298
申请日：2019-11-25
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Shanhui Sun , Terrence Chen
Abstract: Methods and systems for enhancing a distributed medical network. For example, a computer-implemented method includes inputting training data corresponding to each local computer into their corresponding machine learning model; generating a plurality of local losses including generating a local loss for each machine learning model based at least in part on the corresponding training data; generating a plurality of local parameter gradients including generating a local parameter gradient for each machine learning model based at least in part on the corresponding local loss; generating a global parameter update based at least in part on the plurality of local parameter gradients; and updating each machine learning model hosted at each local computer of the plurality of local computers by at least updating their corresponding active parameter set based at least in part on the global parameter update.
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公开(公告)号：US20220172398A1
公开(公告)日：2022-06-02
申请号：US17651808
申请日：2022-02-20
Inventor： Ziyan Wu , Srikrishna Karanam
IPC: G06T7/73
Abstract: A system for patient positioning is provided. The system may acquire image data relating to a patient holding a posture and a plurality of patient models. Each patient model may represent a reference patient holding a reference posture, and include at least one reference interest point of the referent patient and a reference representation of the reference posture. The system may also identify at least one interest point of the patient from the image data using an interest point detection model. The system may further determine a representation of the posture of the patient based on a comparison between the at least one interest point of the patient and the at least one reference interest point in each of the plurality of patient models.
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公开(公告)号：US20220165396A1
公开(公告)日：2022-05-26
申请号：US17666319
申请日：2022-02-07
Inventor： Srikrishna Karanam , Ziyan Wu , Georgios Georgakis
IPC: G16H30/40 , G06T7/00 , G06T7/90 , G06T17/00 , G06T7/50 , G06T7/70 , G06K9/62 , G06T17/20 , G16H10/60 , G16H30/20 , A61B5/00 , G06V10/40 , G06V10/42 , G06V20/64 , G06V40/10 , G06V40/20
Abstract: Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.
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