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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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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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25.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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Patent Agency Ranking
公开(公告)号：US12062198B2
公开(公告)日：2024-08-13
申请号：US17723863
申请日：2022-04-19
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Shubao Liu , Junjie Bai , Youbing Yin , Feng Gao , Yue Pan , Qi Song
IPC: G06T7/33 , A61B6/00 , A61B8/08 , G06T7/00 , G06T7/73 , G06T11/00 , G06T17/00 , G06T19/20 , A61B5/00 , A61B5/055 , A61B6/03 , A61B8/12
CPC classification number: G06T7/344 , A61B6/5247 , A61B8/5261 , G06T7/0012 , G06T7/75 , G06T11/00 , G06T17/00 , G06T19/20 , A61B5/0066 , A61B5/055 , A61B6/032 , A61B8/12 , G06T2207/10081 , G06T2207/10088 , G06T2207/10101 , G06T2207/10132 , G06T2207/20221 , G06T2207/30096 , G06T2207/30101 , G06T2210/41 , G06T2219/2004
Abstract: Embodiments of the disclosure provide methods and systems for multi-modality joint analysis of a plurality of vascular images. The exemplary system may include a communication interface configured to receive the plurality of vascular images acquired using a plurality of imaging modalities. The system may further include at least one processor, configured to extract a plurality of vessel models for a vessel of interest from the plurality of vascular images. The plurality of vessel models are associated with the plurality of imaging modalities, respectively. The at least one processor is also configured to fuse the plurality of vessel models associated with the plurality of imaging modalities to generate a fused model for the vessel of interest. The at least one processor is further configured to provide a diagnostic analysis result based on the fused model of the vessel of interest.
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公开(公告)号：US12026877B2
公开(公告)日：2024-07-02
申请号：US17482901
申请日：2021-09-23
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Jinchen Li , Guang Li , Chengwei Sun , Kunlin Cao , Qi Song
IPC: G06T7/00 , G06F18/2431 , G06N3/04 , G16H30/40
CPC classification number: G06T7/0012 , G06F18/2431 , G06N3/04 , G16H30/40 , G06T2207/10016 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30061
Abstract: The present disclosure provides a method, a device, and a non-transitory computer-readable storage medium for detecting a medical condition of an organ. The method includes obtaining 2D image sequences of the organ in a plurality of different directions and applying a plurality of classification branches to the 2D image sequences. Each classification branch receives a 2D image sequence of one direction and provides a classification result with respect to that direction. Each classification branch includes a convolutional neural network configured to extract first image features from the corresponding 2D image sequence and a recurrent neural network configured to extract second image features from the first image features. The method further includes fusing the classification results provided by the plurality of classification branches for detecting the medical condition.
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28.发明申请公开(公告)号：US20230097133A1
公开(公告)日：2023-03-30
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
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：US20220301156A1
公开(公告)日：2022-09-22
申请号：US17591758
申请日：2022-02-03
Inventor： Zhenghan Fang , Junjie Bai , Youbing Yin , Xinyu Guo , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device. The system may additionally include at least one processor configured to apply the learning model to perform an image analysis task on the medical image. The learning model is trained jointly with an error estimator using training images comprising a first set of labeled images and a second set of unlabeled images. The error estimator is configured to estimate an error of the learning model associated with performing the image analysis task.
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公开(公告)号：US20220222812A1
公开(公告)日：2022-07-14
申请号：US17482901
申请日：2021-09-23
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Jinchen LI , Guang LI , Chengwei SUN , Kunlin CAO , Qi SONG
Abstract: The present disclosure provides a method, a device, and a non-transitory computer-readable storage medium for detecting a medical condition of an organ. The method includes obtaining 2D image sequences of the organ in a plurality of different directions and applying a plurality of classification branches to the 2D image sequences. Each classification branch receives a 2D image sequence of one direction and provides a classification result with respect to that direction. Each classification branch includes a convolutional neural network configured to extract first image features from the corresponding 2D image sequence and a recurrent neural network configured to extract second image features from the first image features. The method further includes fusing the classification results provided by the plurality of classification branches for detecting the medical condition.
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