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：US20220215956A1

公开(公告)日：2022-07-07

申请号：US17567486

申请日：2022-01-03

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

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

IPC: G16H50/20 ,  G06N5/04 ,  G06V10/774 ,  G06T7/00

Abstract: The disclosure relates to a system and method for predicting physiological-related parameters based on a medical image. The method includes receiving a medical image acquired by an image acquisition device and predicting a sequence of physiological-related parameters at a sequence of positions and simultaneously estimating an uncertainty level of the predicted sequence of physiological parameters from the medical image by using a sequential learning model. The sequential learning model is trained to minimize a loss function associated with the uncertainty level. The method not only provides predictions but also the corresponding uncertainty estimations by using sequential learning model(s), thus improving the transparency and explainability of the sequential learning model.

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：US11495357B2

公开(公告)日：2022-11-08

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 ,  A61B6/03 ,  G06T7/00 ,  A61B5/02

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：US20220215958A1

公开(公告)日：2022-07-07

申请号：US17568084

申请日：2022-01-04

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

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

IPC: G16H50/20 ,  G16H30/40 ,  G06T7/00 ,  G06N20/20

Abstract: The present disclosure relates to training methods for a machine learning model for physiological analysis. The training method may include receiving training data including a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter. The training method further includes training, by at least one processor, an initial machine learning model using the first dataset, and applying, by the at least one processor, the initial machine learning model to the second dataset to generate a third dataset of pseudo-labeled data of the physiological-related parameter. The training method also includes training, by the at least one processor, the machine learning model based on the first dataset and the third dataset, and providing the trained machine learning model for predicting the physiological-related parameter. Thereby, the weakly-labeled dataset may be sufficiently utilized in training of the machine learning model and improve ts p iformance.

公开(公告)号：US20210279906A1

公开(公告)日：2021-09-09

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: 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 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.