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21.发明授权公开(公告)号：US11308362B2
公开(公告)日：2022-04-19
申请号：US16827613
申请日：2020-03-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Qi Song , Junjie Bai , Yi Lu , Yi Wu , Feng Gao , Kunlin Cao
Abstract: Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object by tracing a sequence of patches with a virtual agent. For each patch other than the initial patch, the method determines a current patch based on the position and action of the virtual agent at a previous patch. The method further determines a policy function and a value function based on the current patch using a trained learning network, which includes an encoder followed by a first learning network and a second learning network. The learning network is trained by maximizing a cumulative reward. The method also determines the action of the virtual agent at the current patch. Additionally, the method displays the centerline of the object.
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公开(公告)号：US20210374950A1
公开(公告)日：2021-12-02
申请号：US17121595
申请日：2020-12-14
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Feng Gao , Zhenghan Fang , Yue Pan , Junjie Bai , Youbing Yin , Hao-Yu Yang , Kunlin Cao , Qi Song
Abstract: The disclosure relates to systems and methods for vessel image analysis. The method includes receiving a set of images along a vessel acquired by a medical imaging device, and determining a sequence of centerline points along the vessel and a sequence of image patches at the respective centerline points based on the set of images. The method further includes detecting plaques based on the sequence of image patches using a first learning network. The first learning network includes an encoder configured to extract feature maps based on the sequence of image patches and a plaque range generator configured to generate a start position and an end position of each plaque based on the extracted feature maps. The method also includes classifying each detected plaque and determining a stenosis degree for the detected plaque, using a second learning network reusing at least part of the parameters of the first learning network and the extracted feature maps.
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公开(公告)号：US11076824B1
公开(公告)日：2021-08-03
申请号：US17067181
申请日：2020-10-09
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Bin Kong , Yi Lu , Junjie Bai , Zhenghan Fang , Qi Song
IPC: A61B6/00 , G06T7/00 , A61B5/00 , A61B6/03 , G16H50/20 , G16H10/60 , G06N3/04 , G06N3/08 , G16H30/40 , A61B6/02
Abstract: Embodiments of the disclosure provide methods and systems for detecting COVID-19 from a lung image of a patient. The exemplary system may include a communication interface configured to receive the lung image acquired of the patient's lung by an image acquisition device. The system may further include at least one processor, configured to determine a region of interest comprising the lung from the lung image and apply a COVID-19 detection network to the region of interest to determine a condition of the lung. The COVID-19 detection network is a multi-class classification learning network that labels the region of interest as one of COVID-19, non-COVID-19 pneumonia, non-pneumonia abnormal, or normal. The at least one processor is further configured to provide a diagnostic output based on the determined condition of the lung.
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公开(公告)号：US10580526B2
公开(公告)日：2020-03-03
申请号：US15870811
申请日：2018-01-12
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Bin Ma , Xiaoxiao Liu , Yujie Zhou , Youbing Yin , Yuwei Li , Shubao Liu , Xiaoyang Xu , Qi Song
Abstract: The present disclosure relates to a device, a system, and a computer-readable medium for calculating vessel flow parameters based on angiography. In one implementation, the device includes a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: selecting a plurality of template frames from the angiographic images to generate a 3D model for a vessel; determining a start frame and an end frame in the plurality of angiographic images showing a contrast filling process; determining corresponding locations of front ends of the contrast in the start frame and the end frame in the 3D model of the vessel; calculating a vessel volume between the determined locations of the front ends in the 3D model; and determining an average blood flow rate based on the calculated volume, and a time interval between the start frame and the end frame.
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公开(公告)号：US20200065989A1
公开(公告)日：2020-02-27
申请号：US16550093
申请日：2019-08-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Zhihui Guo , Youbing Yin , Xin Wang , Yi Lu , Kunlin Cao , Qi Song , Xiaoyang Xu , Bin Ouyang
Abstract: Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes generating a distance cost image using a trained first learning network based on the image. The method further includes detecting end points of the object using a trained second learning network based on the image. Moreover, the method includes extracting the centerline of the object based on the distance cost image and the end points of the object.
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Patent Agency Ranking
公开(公告)号：US20190362494A1
公开(公告)日：2019-11-28
申请号：US16056535
申请日：2018-08-07
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Kunlin Cao , Yuwei Li , Junjie Bai , Xiaoyang Xu
Abstract: The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.
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27.发明申请公开(公告)号：US20190355120A1
公开(公告)日：2019-11-21
申请号：US16529769
申请日：2019-08-01
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Junjie Bai , Yi Lu , Qi Song , Kunlin Cao
Abstract: Embodiments of the disclosure provide systems and methods for analyzing a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive a learning model and a plurality of model inputs derived from the biomedical image. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to apply the learning model to the plurality of model inputs to analyze the biomedical image. The learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively. The second network is a tree structure network that models a spatial constraint of the tree structure object.
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28.发明授权公开(公告)号：US11869142B2
公开(公告)日：2024-01-09
申请号：US17557373
申请日：2021-12-21
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junjie Bai , Shubao Liu , Youbing Yin , Feng Gao , Yue Pan , Qi Song
CPC classification number: G06T17/00 , G06T7/50 , G06T2207/10116 , G06T2207/30101 , G06T2210/41
Abstract: The disclosure provides a method, device and a computer-readable medium for performing three-dimensional blood vessel reconstruction. The device includes an interface configured to receive a single-view two-dimensional image of a blood vessel of a patient, where the single-view two-dimensional image is a projection image acquired in a predetermined projection direction. The device further includes a processor configured to estimate three-dimensional information of the blood vessel from the single-view two-dimensional image using an inference model, and reconstruct a three-dimensional model of the blood vessel based on the three-dimensional information.
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公开(公告)号：US11495357B2
公开(公告)日：2022-11-08
申请号：US17107881
申请日：2020-11-30
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Bin Ma , Ying Xuan Zhi , Xiaoxiao Liu , Xin Wang , Youbing Yin , Qi Song
Abstract: The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.
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公开(公告)号：US20220284571A1
公开(公告)日：2022-09-08
申请号：US17505917
申请日：2021-10-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Hao-Yu Yang , Junjie Bai , Youbing Yin , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject.
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