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公开(公告)号：US20220351374A1
公开(公告)日：2022-11-03
申请号：US17725051
申请日：2022-04-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin WANG , Youbing YIN , Bin KONG , Yi LU , Xinyu GUO , Hao-Yu YANG , Qi SONG
IPC: G06T7/00 , G06V10/764 , G06V10/82 , G06V10/40 , G06V10/776 , G16H50/20 , G16H50/30
Abstract: This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.
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2.发明申请公开(公告)号：US20200085395A1
公开(公告)日：2020-03-19
申请号：US16689048
申请日：2019-11-19
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Qi SONG , Ying Xuan ZHI , Xiaoxiao LIU , Shubao LIU , Youbing YIN , Yuwei LI , Kunlin CAO
Abstract: The present disclosure relates to a method, storage medium, and system for analyzing an image sequence of a periodic physiological activity. In one implementation, the method includes receiving the image sequence acquired by an imaging device, the image sequence having a plurality of frames, and identifying a feature point in a first frame. The method further includes determining motion vectors for the feature point in the frames of the image sequence. Each motion vector for the feature point is determined based on respective locations of corresponding feature points in frames adjacent to the first frame. The method also includes determining a motion magnitude profile based on the determined motion vectors and determining a phase of each frame in the image sequence based on the motion magnitude profile.
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Patent Agency Ranking
3.发明申请公开(公告)号：US20190209113A1
公开(公告)日：2019-07-11
申请号：US15864398
申请日：2018-01-08
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Xiaoxiao LIU , Shubao LIU , Bin MA , Kunlin CAO , Youbing YIN , Yuwei LI , Qian ZHAO , Qi SONG
CPC classification number: A61B6/504 , G06F16/50 , G06T7/20 , G06T2207/30004
Abstract: The present disclosure relates to a method, storage medium, and system for analyzing an image sequence of a periodic physiological activity. In one implementation, the method includes receiving the image sequence acquired by an imaging device, the image sequence having a plurality of frames and determining local motions for pixels in each frame of the image sequence. The local motion for a pixel may be determined using corresponding pixels in frames adjacent to the frame to which the pixel belongs. The method further includes determining principal motions for the plurality of frames based on the local motions; determining a motion magnitude profile based on the principal motions; and determining the phase of each frame in the image sequence based on the motion magnitude profile.
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公开(公告)号：US20220392059A1
公开(公告)日：2022-12-08
申请号：US17558756
申请日：2021-12-22
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Bin KONG , Xin WANG , Youbing YIN , Hao-Yu YANG , Yi LU , Xinyu GUO , Qi SONG
Abstract: Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.
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公开(公告)号：US20220344033A1
公开(公告)日：2022-10-27
申请号：US17726039
申请日：2022-04-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin WANG , Youbing YIN , Bin KONG , Yi LU , Xinyu GUO , Hao-Yu YANG , Junjie BAI , Qi SONG
Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.
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公开(公告)号：US20220351863A1
公开(公告)日：2022-11-03
申请号：US17726307
申请日：2022-04-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin WANG , Youbing YIN , Bin KONG , Yi LU , Hao-Yu YANG , Xinyu GUO , Qi SONG
Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure. Then, an embed feature is obtained based on both the local feature and the global feature and input into to the node. The method is able to integrate local and global consideration factors of the sampling points into the GNN to improve the prediction accuracy.
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公开(公告)号：US20190304592A1
公开(公告)日：2019-10-03
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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