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公开(公告)号：US20210158512A1
公开(公告)日：2021-05-27
申请号：US17039355
申请日：2020-09-30
Inventor： Shanhui Sun , Hanchao Yu , Xiao Chen , Zhang Chen , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with imagery data processing. The neural networks may be pre-trained to learn parameters or models for processing the imagery data and upon deployment the neural networks may automatically perform further optimization of the learned parameters or models based on a small set of online data samples. The online optimization may be facilitated via offline meta-learning so that the optimization may be accomplished quickly in a few optimization steps.
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公开(公告)号：US10887558B1
公开(公告)日：2021-01-05
申请号：US16564472
申请日：2019-09-09
Inventor： Arun Innanje , Abhishek Sharma , Ziyan Wu , Terrence Chen
Abstract: Methods and systems for automatically setting up a sensor connected to an apparatus. For example, a computer-implemented method for automatically setting up a sensor connected to an apparatus includes: receiving a sensor-connection signal corresponding to a connection established between the sensor and the apparatus; determining whether a streaming microservice corresponding to the sensor has been downloaded onto the apparatus; if the streaming microservice has not been downloaded onto the apparatus, determining whether the streaming microservice corresponding to the sensor is supported by the apparatus; if the streaming microservice is supported by the apparatus, downloading a streaming microservice docker from a docker registry, the streaming microservice docker including the streaming microservice and a driver corresponding to the sensor; and deploying the streaming microservice with the driver corresponding to the sensor.
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公开(公告)号：US20250025054A1
公开(公告)日：2025-01-23
申请号：US18223414
申请日：2023-07-18
Inventor： Yikang Liu , Dehong Fang , Lin Zhao , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Described herein are systems, methods, and instrumentalities associated with automatic determination of hemodynamic characteristics. An apparatus as described may implement a first artificial neural network (ANN) and a second ANN. The first ANN may model a mapping from a set of 3D points associated with one or more blood vessels to a set of hemodynamic characteristics of the one or more blood vessels, while the second ANN may generate, based on a geometric relationship of the set of points in a 3D space, parameters for controlling the mapping. The apparatus may obtain a 3D anatomical model representing at least one blood vessel of a patient based on one or more medical images of the patient, and determine, based on the first ANN and the second ANN, a hemodynamic characteristic of the at least one blood vessel of the patient at a target location of the 3D anatomical model.
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公开(公告)号：US12178641B2
公开(公告)日：2024-12-31
申请号：US17814223
申请日：2022-07-21
Inventor： Shanhui Sun , Ziyan Wu , Xiao Chen , Zhang Chen , Yikang Liu , Arun Innanje , Terrence Chen
Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.
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公开(公告)号：US20240341903A1
公开(公告)日：2024-10-17
申请号：US18134234
申请日：2023-04-13
Inventor： Abhishek Sharma , Arun Innanje , Terrence Chen
CPC classification number: A61B90/36 , A61B34/10 , A61B2034/105 , A61B2090/365
Abstract: An object or person in a medical environment may be identified based on images of the medical environment. The identification may include determining an identifier associated with the object or the person, a position of the object or the person in the medical environment, and a three-dimensional (3D) shape/pose of the object or the person. Representation information that indicates at least the determined identifier, position in the medical environment, and 3D shape/pose of the object or the person may be generated and then used (e.g., by a visualization device) together with one or more predetermined 3D models to determine a 3D model for the object or the person identified in the medical environment and generate a visual depiction of at least the object or the person in the medical environment based on the determined 3D model and the position of the object or the person in the medical environment.
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Patent Agency Ranking
公开(公告)号：US20240331446A1
公开(公告)日：2024-10-03
申请号：US18126853
申请日：2023-03-27
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
CPC classification number: G06V40/20 , G06V10/26 , G06V10/40 , G06V10/82 , G06V40/11 , G06V10/774 , G06V2201/03
Abstract: Automatic hand gesture determination may be a challenging task considering the complex anatomy and high dimensionality of the human hand. Disclosed herein are systems, methods, and instrumentalities associated with recognizing a hand gesture in spite of the challenges. An apparatus in accordance with embodiments of the present disclosure may use machine learning based techniques to identify the area of an image that may contain a hand and to determine an orientation of the hand relative to a pre-defined direction. The apparatus may then adjust the area of the image containing the hand to align the orientation of the hand with the pre-defined direction and/or to scale the image area to a pre-defined size. Based on the adjusted image area, the apparatus may detect a plurality of hand landmarks and predict a gesture indicated by the hand based on the plurality of detected landmarks.
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公开(公告)号：US20240331222A1
公开(公告)日：2024-10-03
申请号：US18130150
申请日：2023-04-03
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Yikang Liu , Terrence Chen
IPC: G06T11/00
CPC classification number: G06T11/005 , G06T2210/41 , G06T2211/424
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with magnetic resonance (MR) image reconstruction. An under-sampled MR image may be reconstructed through an iterative process (e.g., over multiple iterations) based on a machine-learning (ML) model. The ML model may be obtained through a reinforcement learning process during which the ML model may be used to predict a correction to an input MR image of at least one of the multiple iterations, apply the correction to the input MR image to obtain a reconstructed MR image, determine a reward for the ML model based on the reconstructed MR image, and adjust the parameters of the ML model based on the reward. The reward may be determined using a pre-trained reward neural network and the ML model may also be pre-trained in a supervised manner before being refined through the reinforcement learning process.
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公开(公告)号：US20240303832A1
公开(公告)日：2024-09-12
申请号：US18119435
申请日：2023-03-09
Inventor： Xiao Chen , Kun Han , Zhang Chen , Yikang Liu , Shanhui Sun , Terrence Chen
CPC classification number: G06T7/251 , G06T7/215 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048
Abstract: The motion estimation of an anatomical structure may be performed using a machine-learned (ML) model trained based on medical training images of the anatomical structure and corresponding segmentation masks for the anatomical structure. During the training of the ML model, the model may be used to predict a motion field that may indicate a change between a first training image and a second training image, and to transform the first training image and a corresponding first segmentation mask based on the motion field. The parameters of the ML model may then be adjusted to maintain a correspondence between the transformed first training image and the second training image and between the transformed first segmentation mask or a second segmentation mask associated with the second training image. The correspondence may be assessed based on at least a boundary region shared by the anatomical structure and one or more other anatomical structures.
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公开(公告)号：US20240296552A1
公开(公告)日：2024-09-05
申请号：US18117068
申请日：2023-03-03
Inventor： Xiao Chen , Shanhui Sun , Zhang Chen , Yikang Liu , Arun Innanje , Terrence Chen
CPC classification number: G06T7/0012 , G06T7/10 , G16H30/40 , G06T2207/30048
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with cardiac motion tracking and/or analysis. In accordance with embodiments of the disclosure, the motion of a heart such as an anatomical component of the heart may be tracked through multiple medical images and a contour of the anatomical component may be outlined in the medical images and presented to a user. The user may adjust the contour in one or more of the medical images and the adjustment may trigger modifications of motion field(s) associated with the one or more medical images, re-tracking of the contour in the one or more medical images, and/or re-determination of a physiological characteristic (e.g., a myocardial strain) of the heart. The adjustment may be made selectively, for example, to a specific medical image or one or more additional medical images selected by the user, without triggering a modification of all of the medical images.
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公开(公告)号：US12073539B2
公开(公告)日：2024-08-27
申请号：US17564348
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T5/70 , G06N3/045 , G06N3/08 , G06T5/50 , G16H30/40 , G06T2207/10064 , G06T2207/20081 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with denoising medical images such as fluoroscopic images using deep learning techniques. A first artificial neural network (ANN) is trained to denoise an input medical image in accordance with a provided target noise level. The training of the first ANN is conducted by pairing a noisy input image with target denoised images that include different levels of noise. These target denoised images are generated using a second ANN as intermediate outputs of the second ANN during different training iterations. As such, the first ANN may learn to perform the denoising task in an unsupervised manner without requiring noise-free training images as the ground truth.
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