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公开(公告)号：US20240177420A1
公开(公告)日：2024-05-30
申请号：US17994675
申请日：2022-11-28
发明人： Srikrishna Karanam , Runze Li , Meng Zheng , Ziyan Wu
CPC分类号： G06T17/20 , A61B34/10 , G06T7/75 , A61B2034/105 , G06T2207/30196
摘要： A video sequence depicting a person in a medical environment may be obtained and used for determining one or more human models of the person. A first human model representing a first pose or a first body shape of the person may be determined based on a first subset of images from the video sequence, while a second human model representing a second pose or a second body shape of the person may be determined based on a second subset of images from the video sequence. The second 2D or 3D representation of the person may include an adjustment to the first 2D or 3D representation of the person based on the observation of the person provided by the second subset of images.
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公开(公告)号：US20240161440A1
公开(公告)日：2024-05-16
申请号：US17988328
申请日：2022-11-16
发明人： Meng Zheng , Yuchun Liu , Fan Yang , Srikrishna Karanam , Ziyan Wu , Terrence Chen
CPC分类号： G06V10/24 , G06T7/80 , G06V10/751 , G06V10/82 , G06T2207/10024 , G06T2207/20081
摘要： Images captured by different image capturing devices may have different fields of views and/or resolutions. One or more of these images may be aligned based on an image template, and additional details for the adapted images may be predicted using a machine-learned data recovery model and added to the adapted images such that the images may have the same field of view or the same resolution.
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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
发明人： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC分类号： 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
摘要： 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：US20230169657A1
公开(公告)日：2023-06-01
申请号：US17538232
申请日：2021-11-30
发明人： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
CPC分类号： G06T7/0014 , G06T7/74 , G06T7/50 , G16H50/50 , G16H30/40 , G06N3/08 , G06T2207/10028 , G06T2207/30004 , G06T2207/20084 , G06T2207/20081
摘要： 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
发明人： Srikrishna Karanam , Meng Zheng , Ziyan Wu
摘要： 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：US20240233419A9
公开(公告)日：2024-07-11
申请号：US18128290
申请日：2023-03-30
发明人： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC分类号： G06V20/70 , G06V10/235
摘要： 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：US20240099774A1
公开(公告)日：2024-03-28
申请号：US17955279
申请日：2022-09-28
发明人： Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
CPC分类号： A61B34/10 , A61B34/30 , G06T17/20 , G16H50/50 , A61B2034/105 , A61B2034/107
摘要： Systems, methods and instrumentalities are described herein for automatically devising and executing a surgical plan associated with a patient in a medical environment, e.g., under the supervision of a medical professional. The surgical plan may be devised based on images of the medical environment captured by one or more sensing devices. A processing device may determine, based on all or a first subset of the images, a patient model that may indicate a location and a shape of an anatomical structure of the patient and determine, based on all or a second subset of the images, an environment model that may indicate a three-dimensional (3D) spatial layout of the medical environment. The surgical plan may be devised based on the patient model and the environment model, and may indicate at least a movement path of a medical device towards the anatomical structure of the patient.
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8.发明公开公开(公告)号：US20230419740A1
公开(公告)日：2023-12-28
申请号：US17851190
申请日：2022-06-28
发明人： Benjamin Planche , Ziyan Wu , Meng Zheng , Terrence Chen
CPC分类号： G06V40/70 , G06T7/0012 , G06V40/15 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/10116 , G06T2207/10081
摘要： A non-invasive biometric system includes a processor that is configured to control a scanner, which is configured to scan and capture one or more anatomical images of a body of a target person. The processor is further configured to identify one or more anatomical structures in the captured one or more anatomical images and extract anatomical features for the identified one or more anatomical structures. The processor is further configured to register the extracted anatomical features for the identified one or more identified anatomical structures to a posture and an external appearance of the target person. The processor is further configured to encode and utilize the extracted anatomical features as biometric data, which is unique for the target person, and may be used for authentication of the target person.
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公开(公告)号：US20230202044A1
公开(公告)日：2023-06-29
申请号：US17564919
申请日：2021-12-29
发明人： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
CPC分类号： B25J9/1676 , G06V10/768 , G06V20/64 , G06T7/215 , B25J13/08 , G05D1/0253 , A61B6/102
摘要： An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.
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公开(公告)号：US20230132936A1
公开(公告)日：2023-05-04
申请号：US18149111
申请日：2023-01-01
发明人： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma , Ren Li
摘要： 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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