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：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.