公开(公告)号：US12026881B2

公开(公告)日：2024-07-02

申请号：US17567458

申请日：2022-01-03

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Bin Kong ,  Youbing Yin ,  Xin Wang ,  Yi Lu ,  Haoyu Yang ,  Junjie Bai ,  Qi Song

IPC: G06T7/12 ,  G06F18/213 ,  G06F18/214 ,  G06T7/00 ,  G06T7/73 ,  G06V10/44

CPC classification number: G06T7/0012 ,  G06F18/213 ,  G06F18/214 ,  G06T7/75 ,  G06V10/44 ,  G06T2207/20081 ,  G06T2207/30104 ,  G06V2201/03

Abstract: Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.

公开(公告)号：US11847547B2

公开(公告)日：2023-12-19

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

IPC: G06T15/00 ,  G06N3/006 ,  G06N3/08 ,  G06T19/00 ,  G06V10/42 ,  G06F18/21 ,  G06V10/776

CPC classification number: G06N3/006 ,  G06F18/217 ,  G06N3/08 ,  G06T19/00 ,  G06V10/42 ,  G06V10/776

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.

公开(公告)号：US20220091568A1

公开(公告)日：2022-03-24

申请号：US17468040

申请日：2021-09-07

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Bin Kong ,  Youbing Yin ,  Xin Wang ,  Yi Lu ,  Qi Song

IPC: G05B13/04 ,  G05B13/02

Abstract: The disclosure relates to a method and device for predicting a physical parameter based on input physical information, and medium. The method may include predicting, by a processor, an intermediate variable based on the input physical information with an intermediate sub-model, which incorporates a constraint on the intermediate variable according to prior information of the physical parameter. The method may also include transforming, by the processor, the intermediate variable predicted by the intermediate sub-model to the physical parameter with a transformation sub-model.

公开(公告)号：US20210082580A1

公开(公告)日：2021-03-18

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

IPC: G16H50/50 ,  A61B5/0275 ,  A61B5/026 ,  A61B6/00

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.

公开(公告)号：US10937549B2

公开(公告)日：2021-03-02

申请号：US16048272

申请日：2018-07-28

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Bin Ma ,  Ying Xuan Zhi ,  Xiaoxiao Liu ,  Xin Wang ,  Youbing Yin ,  Qi Song

IPC: G06T7/00 ,  G16H50/50 ,  A61B5/0275 ,  A61B5/026 ,  A61B6/00 ,  A61B6/03 ,  A61B5/02

Abstract: The present disclosure is directed to a method and device for automatically predicting FFR based on images of vessel. The method for automatically predicting FFR based on images of a vessel. The method comprises a step of receiving the images of a vessel acquired by an imaging device. Then, a sequence of flow speeds at a sequence of positions on a centerline of the vessel is acquired by a processor. A sequence of first features at the sequence of positions on a centerline of the vessel are acquired by the processor, by fusing structure-related features and flow speeds and using a convolutional neural network. Then, a sequence of FFR at the sequence of positions is determined by the processor through using a sequence-to-sequence neural network on the basis of the sequence of first features.

公开(公告)号：US10430949B1

公开(公告)日：2019-10-01

申请号：US16392516

申请日：2019-04-23

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Junjie Bai ,  Yi Lu ,  Qi Song

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

Abstract: Embodiments of the disclosure provide systems and methods for segmenting a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive the biomedical image and a learning model. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to extract a plurality of image patches from the biomedical image and apply the learning model to the plurality of image patches to segment the biomedical image. The learning model includes a convolutional network configured to process the plurality of image patches to construct respective feature maps and a tree structure network configured to process the feature maps collectively to obtain a segmentation mask for the tree structure object. The tree structure network models a spatial constraint of the plurality of image patches.

公开(公告)号：US11748902B2

公开(公告)日：2023-09-05

申请号：US17330557

申请日：2021-05-26

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: G06K9/00 ,  G06T7/68 ,  G06N3/08 ,  G06N3/045

CPC classification number: G06T7/68 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30101 ,  G06T2207/30172

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 detecting at least one bifurcation of the object using a trained bifurcation learning network based on the image. The method further includes extracting the centerline of the object based on a constraint condition that the centerline passes through the detected bifurcation.

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

IPC: G06K9/62 ,  G06N3/08 ,  G06T19/00 ,  G06V10/42

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.

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

IPC: G06T15/00 ,  G06K9/62 ,  G06N3/08 ,  G06T19/00 ,  G06V10/42

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：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.