-
公开(公告)号：US20230098121A1
公开(公告)日：2023-03-30
申请号：US17489682
申请日：2021-09-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Feng Gao , Hao-Yu Yang , Yue Pan , Youbing Yin , Qi Song
IPC: G16H50/20 , G16H10/60 , G16H50/30 , G16H50/50 , G16H30/40 , G06F16/23 , A61B5/00 , A61B5/02 , A61B5/107
Abstract: The disclosure relates to a method for prognosis management based on medical information of a patient, a device, and a medium. The method includes acquiring, by a processor, medical information of the patient at a first time. The method further includes receiving the medical information of the patient at a first time. The method may further include predicting, by a processor, a progression condition of an object associated with the patient at a second time based on the acquired medical information of the first time. The progression condition is indicative of a prognosis risk, and the second time is after the first time. The method may also include outputting the predicted progression condition to an information management system. The method is helpful for users to understand the potential prognosis risk of the object at the second time to aid users in making treatment decisions.
-
12.发明申请公开(公告)号：US20220198226A1
公开(公告)日：2022-06-23
申请号：US17692337
申请日：2022-03-11
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 a processor, using a reinforcement learning network configured to predict movement of a virtual agent that traces the centerline in the image. The reinforcement learning network is further configured to perform at least one auxiliary task that detects a bifurcation in a trajectory of the object. The reinforcement learning network is trained by maximizing a cumulative reward and minimizing an auxiliary loss of the at least one auxiliary task. Additionally, the method includes displaying the centerline of the object.
-
13.发明授权公开(公告)号：US11341631B2
公开(公告)日：2022-05-24
申请号：US16028389
申请日：2018-07-05
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Qi Song , Shanhui Sun , Feng Gao , Junjie Bai , Hanbo Chen , Youbing Yin
Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.
-
14.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.
-
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.
-
Patent Agency Ranking
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.
-
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.
-
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.
-
19.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.
-
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.
-
-
-
-
-
-
-
-
-
Search Results
57
records
(took 0.021 seconds)
