公开(公告)号：US11069078B2

公开(公告)日：2021-07-20

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: G06K9/00 ,  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：US10573005B2

公开(公告)日：2020-02-25

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 ,  G06T7/174 ,  G06T7/00

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：US20190325579A1

公开(公告)日：2019-10-24

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 ,  G06N20/00 ,  G06N3/08

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：US12119117B2

公开(公告)日：2024-10-15

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

IPC: G16H50/30 ,  G06N3/045 ,  G06T7/00 ,  G06V10/42 ,  G06V10/44 ,  G06V10/82 ,  G16H30/40

CPC classification number: G16H50/30 ,  G06N3/045 ,  G06T7/0012 ,  G06V10/42 ,  G06V10/44 ,  G06V10/82 ,  G16H30/40

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.

公开(公告)号：US12094596B2

公开(公告)日：2024-09-17

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

IPC: G06V10/82 ,  G06N3/045 ,  G06V20/70 ,  G16H30/40

CPC classification number: G16H30/40 ,  G06N3/045 ,  G06V10/82 ,  G06V20/70

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.

公开(公告)号：US11538161B2

公开(公告)日：2022-12-27

申请号：US17015070

申请日：2020-09-08

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Kunlin Cao ,  Yuwei Li ,  Junjie Bai ,  Xiaoyang Xu

IPC: G06T7/00 ,  A61B6/00 ,  G06K9/62 ,  G06T17/00 ,  G06V10/44

Abstract: The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.

公开(公告)号：US20220215535A1

公开(公告)日：2022-07-07

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: G06T7/00 ,  G06V10/44 ,  G06K9/62 ,  G06T7/73

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：US11361440B2

公开(公告)日：2022-06-14

申请号：US17317989

申请日：2021-05-12

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

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

IPC: G06T7/00 ,  A61B6/00 ,  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 disease condition prediction from images of a patient. The system may include a communication interface configured to receive a sequence of images acquired of the patient by an image acquisition device. The sequence of images are acquired at a sequence of prior time points during progression of a disease. The system may include a processor, configured to determine regions of interest based on the sequence of images. The processor applies a progressive condition prediction network to the regions of interest to predict a level of disease progression at a future time point during the progression of the disease. The progressive condition prediction network predicts the level of disease progression based on the regions of interest and disease conditions at the sequence of prior time points. The processor further provides a diagnostic output based on the predicted level of disease progression.