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公开(公告)号：US20210158937A1
公开(公告)日：2021-05-27
申请号：US16860901
申请日：2020-04-28
Inventor： Ziyan Wu , Srikrishna Karanam , Arun Innanje , Shanhui Sun , Abhishek Sharma , Yimo Guo , Zhang Chen
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：US20210158028A1
公开(公告)日：2021-05-27
申请号：US16995446
申请日：2020-08-17
Inventor： Ziyan Wu , Srikrishna Karanam , Changjiang Cai , Georgios Georgakis
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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公开(公告)号：US20240233338A9
公开(公告)日：2024-07-11
申请号：US17969876
申请日：2022-10-20
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：US12026913B2
公开(公告)日：2024-07-02
申请号：US17564792
申请日：2021-12-29
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
Abstract: Automatically validating the calibration of an visual sensor network includes acquiring image data from visual sensors that have partially overlapping fields of view, extracting a representation of an environment in which the visual sensors are disposed, calculating one or more geometric relationships between the visual sensors, comparing the calculated one or more geometric relationships with previously obtained calibration information of the visual sensors, and verifying a current calibration of the visual sensors based on the comparison.
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公开(公告)号：US11966852B2
公开(公告)日：2024-04-23
申请号：US16710070
申请日：2019-12-11
Inventor： Ziyan Wu , Srikrishna Karanam , Lidan Wang
CPC classification number: G06N5/02 , G06F9/3836 , G06F17/18 , G06N3/04 , G06N3/08
Abstract: The present disclosure generally provides systems and methods for situation awareness. When executing a set of instructions stored in at least one non-transitory storage medium, at least one processor may be configured to cause the system to perform operations including obtaining, from at least one of one or more sensors, environmental data associated with an environment corresponding to a first time point, generating a first static global representation of an environment corresponding to the first time point based at least in part on the environmental data, generating a first dynamic global representation of the environment corresponding to the first time point based at least in part on the environmental data, and estimating, based on the first static global representation and the first dynamic global representation, a target state of the environment at a target time point using a target estimation model.
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Patent Agency Ranking
公开(公告)号：US11941738B2
公开(公告)日：2024-03-26
申请号：US17513392
申请日：2021-10-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: G06T13/40 , G06T17/20 , G06T19/20 , G06T2210/41 , G06T2219/2004
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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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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