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

IPC: G06T7/68 ,  G06N3/04 ,  G06N3/08

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.

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

IPC: G06T7/00 ,  G06K9/62 ,  G06K9/46 ,  G06T17/00 ,  A61B6/00

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.

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

IPC: G06T7/11 ,  G06N20/00 ,  G06N3/08 ,  G06T7/00 ,  G06T7/174

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.

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

IPC: G06T17/00 ,  G06T7/50

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.

公开(公告)号：US20220301156A1

公开(公告)日：2022-09-22

申请号：US17591758

申请日：2022-02-03

Applicant: Shenzhen Keya Medical Technology Corporation,

Inventor： Zhenghan Fang ,  Junjie Bai ,  Youbing Yin ,  Xinyu Guo ,  Qi Song

IPC: G06T7/00 ,  G16H30/40

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.

公开(公告)号：US11357464B2

公开(公告)日：2022-06-14

申请号：US17317487

申请日：2021-05-11

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Bin Kong ,  Yi Lu ,  Junjie Bai ,  Zhenghan Fang ,  Qi Song

IPC: G06T7/00 ,  A61B6/03 ,  G16H50/20 ,  A61B6/00 ,  A61B5/00 ,  G16H10/60 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40 ,  A61B6/02

Abstract: Embodiments of the disclosure provide methods and systems for determining a disease condition from a 3D image of a patient. The exemplary system may include a communication interface configured to receive the 3D image acquired of the patient by an image acquisition device. The system may further include a processor, configured to determine a 3D region of interest from the 3D image and apply a detection network to the 3D region of interest to determine the disease condition and a severity of the disease condition. The detection network is a multi-task learning network that determines the disease condition based on one or more lesion masks determined from the 3D region of interest and determines the severity of the disease condition from the 3D region of interest. The processor is further configured to provide a diagnostic output based on the disease condition and the severity of the disease condition.

公开(公告)号：US10803583B2

公开(公告)日：2020-10-13

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

IPC: G06T7/00 ,  A61B6/00 ,  G06K9/46 ,  G06K9/62 ,  G06T17/00

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.

公开(公告)号：US20200311485A1

公开(公告)日：2020-10-01

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

IPC: G06K9/62 ,  G06K9/52 ,  G06T19/00 ,  G06N3/08

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：US20190050982A1

公开(公告)日：2019-02-14

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

IPC: G06T7/00 ,  G06T7/70

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.