公开(公告)号：US20220351863A1

公开(公告)日：2022-11-03

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 ,  G16H30/40 ,  G06T7/00 ,  G06V10/44 ,  G06V10/42 ,  G06V10/82 ,  G06N3/04

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：US20220392059A1

公开(公告)日：2022-12-08

申请号：US17558756

申请日：2021-12-22

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Bin KONG ,  Xin WANG ,  Youbing YIN ,  Hao-Yu YANG ,  Yi LU ,  Xinyu GUO ,  Qi SONG

IPC: G06T7/00 ,  G06V10/40 ,  G06V10/764 ,  G06V10/82 ,  G06V10/77 ,  G06T7/73 ,  G06N3/08 ,  G16H30/40

Abstract: Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.

公开(公告)号：US20220344033A1

公开(公告)日：2022-10-27

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: G16H30/40 ,  G06V20/70 ,  G06V10/82 ,  G06N3/04

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：US20220351374A1

公开(公告)日：2022-11-03

申请号：US17725051

申请日：2022-04-20

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin WANG ,  Youbing YIN ,  Bin KONG ,  Yi LU ,  Xinyu GUO ,  Hao-Yu YANG ,  Qi SONG

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/82 ,  G06V10/40 ,  G06V10/776 ,  G16H50/20 ,  G16H50/30

Abstract: This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.