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公开(公告)号：US20240108415A1
公开(公告)日：2024-04-04
申请号：US17957382
申请日：2022-09-30
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: A61B34/20 , A61B34/30 , A61G13/02 , G06T7/73 , G06T17/20 , G16H20/40 , A61B2034/2065 , A61G2203/34 , G06T2207/10024 , G06T2207/10028 , G06T2207/10048 , G06T2207/20081 , G06T2207/30196
Abstract: Disclosed is a method and a system for automatic positioning of a medical equipment with respect to a patient. The method includes obtaining sensor data related to the patient, from a plurality of sensors fixed relative to the medical equipment. The method further includes processing the sensor data to determine at least one pose characteristic of the patient and at least one shape characteristic of the patient. The method further includes determining at least one adjustment parameter for the medical equipment based on the at least one pose characteristic of the patient and the at least one shape characteristic of the patient. The method further includes adjusting the medical equipment based on the at least one adjustment parameter.
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公开(公告)号：US11854232B2
公开(公告)日：2023-12-26
申请号：US17651808
申请日：2022-02-20
Inventor： Ziyan Wu , Srikrishna Karanam
IPC: G06T7/73
CPC classification number: G06T7/75 , G06T7/74 , G06T2207/10024 , G06T2207/10028 , G06T2207/20081 , G06T2207/30004 , G06T2207/30196
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：US11786129B2
公开(公告)日：2023-10-17
申请号：US17666319
申请日：2022-02-07
Inventor： Srikrishna Karanam , Ziyan Wu , Georgios Georgakis
IPC: G06K9/00 , A61B5/00 , G16H30/40 , G06T7/00 , G06T7/90 , G06T17/00 , G06T7/50 , G06T7/70 , G06T17/20 , G16H10/60 , G16H30/20 , G06V20/64 , G06V40/10 , G06V40/20 , G06V20/62 , G06F18/21 , G06F18/214 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82 , G06V10/42 , G06V10/40
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: 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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公开(公告)号：US20230169657A1
公开(公告)日：2023-06-01
申请号：US17538232
申请日：2021-11-30
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
CPC classification number: G06T7/0014 , G06T7/74 , G06T7/50 , G16H50/50 , G16H30/40 , G06N3/08 , G06T2207/10028 , G06T2207/30004 , G06T2207/20084 , G06T2207/20081
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：US20230132479A1
公开(公告)日：2023-05-04
申请号：US17513392
申请日：2021-10-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
Abstract: A three-dimensional (3D) model of a person may be obtained using a pre-trained neural network based on one or more images of the person. Such a model may be subject to estimation bias and/or other types of defects or errors. Described herein are systems, methods, and instrumentalities for refining the 3D model and/or the neural network used to generate the 3D model. The proposed techniques may extract information such as key body locations and/or a body shape from the images and refine the 3D model and/or the neural network using the extracted information. In examples, the 3D model and/or the neural network may be refined by minimizing a difference between the key body locations and/or body shape extracted from the images and corresponding key body locations and/or body shape determined from the 3D model. The refinement may be performed in an iterative and alternating manner.
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Patent Agency Ranking
公开(公告)号：US11461929B2
公开(公告)日：2022-10-04
申请号：US16699059
申请日：2019-11-28
Inventor： Ziyan Wu , Srikrishna Karanam
Abstract: A method for automated calibration is provided. The method may include obtaining a plurality of interest points based on prior information regarding a device and image data of the device captured by a visual sensor. The method may include identifying at least a portion of the plurality of interest points from the image data of the device. The method may also include determining a transformation relationship between a first coordinate system and a second coordinate system based on information of at least a portion of the identified interest points in the first coordinate system and in the second coordinate system that is applied to the visual sensor or the image data of the device.
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公开(公告)号：US12232900B2
公开(公告)日：2025-02-25
申请号：US17560492
申请日：2021-12-23
Inventor： Meng Zheng , Elena Zhao , Srikrishna Karanam , Ziyan Wu , Terrence Chen
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：US20240177326A1
公开(公告)日：2024-05-30
申请号：US17994696
申请日：2022-11-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: G06T7/50 , G06N3/02 , G06T2207/20084
Abstract: A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.
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公开(公告)号：US11948250B2
公开(公告)日：2024-04-02
申请号：US17513534
申请日：2021-10-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: G06T17/20 , A61B34/10 , A61B90/361 , G06T7/70 , A61B2034/105 , A61B2034/107 , A61B2090/367 , G06T2207/30196
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：US11896408B2
公开(公告)日：2024-02-13
申请号：US17525488
申请日：2021-11-12
Inventor： Meng Zheng , Abhishek Sharma , Srikrishna Karanam , Ziyan Wu
CPC classification number: A61B6/0407 , G06F18/24 , G06N20/20 , G06T7/70 , 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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