中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

110 records (took 0.029 seconds)

    Tubular structure segmentation
    21.
    发明授权
    Tubular structure segmentation 有权

    公开(公告)号：US12205277B2

    公开(公告)日：2025-01-21

    申请号：US17564304

    申请日：2021-12-29

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Yikang Liu ,  Shanhui Sun ,  Terrence Chen ,  Zhang Chen ,  Xiao Chen

    IPC: G06T7/00 ,  G06N3/02 ,  G06T7/11 ,  G06T7/194

    Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.

    SYSTEMS AND METHODS FOR SEGMENTING A MEDICAL IMAGE SEQUENCE
    22.
    发明申请
    SYSTEMS AND METHODS FOR SEGMENTING A MEDICAL IMAGE SEQUENCE 有权

    公开(公告)号：US20240420334A1

    公开(公告)日：2024-12-19

    申请号：US18209704

    申请日：2023-06-14

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Yikang Liu ,  Zhang Chen ,  Xiao Chen ,  Shanhui Sun ,  Terrence Chen

    IPC: G06T7/11

    Abstract: An apparatus may obtain a sequence of medical images of a target structure and determine, using a first ANN, a first segmentation and a second segmentation of the target structure based on a first medical image and a second medical image, respectively. The first segmentation may indicate a first plurality of pixels that may belong to the target structure. The second segmentation may indicate a second plurality of pixels that may belong to the target structure. The apparatus may identify, using a second ANN, a first subset of true positive pixels among the first plurality of pixels that may belong to the target structure, and a second subset of true positive pixels among the second plurality of pixels that may belong to the target structure. The apparatus may determine a first refined segmentation and a second refined segmentation of the target structure based on the true positive pixels.

    Image registration
    23.
    发明授权
    Image registration 有权

    公开(公告)号：US12141990B2

    公开(公告)日：2024-11-12

    申请号：US17475534

    申请日：2021-09-15

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Shanhui Sun ,  Zhang Chen ,  Xiao Chen ,  Terrence Chen ,  Junshen Xu

    IPC: G06T7/30 ,  G06N3/02

    Abstract: Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.

    Systems and methods for MRI data processing
    24.
    发明授权
    Systems and methods for MRI data processing 有权

    公开(公告)号：US12141234B2

    公开(公告)日：2024-11-12

    申请号：US17741323

    申请日：2022-05-10

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Xiao Chen ,  Yikang Liu ,  Zhang Chen ,  Shanhui Sun ,  Terrence Chen ,  Daniel Hyungseok Pak

    IPC: G06F18/214 ,  A61B5/00 ,  G01R33/56 ,  G06N20/00 ,  G06T11/00

    Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.

    SYSTEMS AND METHODS FOR AUTOMATIC CARDIAC IMAGE ANALYSIS
    27.
    发明公开
    SYSTEMS AND METHODS FOR AUTOMATIC CARDIAC IMAGE ANALYSIS 审中-公开

    公开(公告)号：US20240144469A1

    公开(公告)日：2024-05-02

    申请号：US17973982

    申请日：2022-10-26

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Xiao Chen ,  Shanhui Sun ,  Terrence Chen ,  Arun Innanje

    IPC: G06T7/00 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764

    CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764 ,  G06T2207/10088 ,  G06T2207/30048

    Abstract: Cardiac images such as cardiac magnetic resonance (CMR) images and tissue characterization maps (e.g., T1/T2 maps) may be analyzed automatically using machine learning (ML) techniques, and reports may be generated to summarize the analysis. The ML techniques may include training one or more of an image classification model, a heart segmentation model, or a cardiac pathology detection model to automate the image analysis and/or reporting process. The image classification model may be capable of grouping the cardiac images into different categories, the heart segmentation model may be capable of delineating different anatomical regions of the heart, and the pathology detection model may be capable of detecting a medical abnormality in one or more of the anatomical regions based on tissue patterns or parameters automatically recognized by the detection model. Image registration that compensates for the impact of motions or movements may also be conducted automatically using ML techniques.

    SYSTEM AND METHOD FOR MAGNIFYING AN IMAGE BASED ON TRAINED NEURAL NETWORKS
    30.
    发明公开
    SYSTEM AND METHOD FOR MAGNIFYING AN IMAGE BASED ON TRAINED NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240087082A1

    公开(公告)日：2024-03-14

    申请号：US17943724

    申请日：2022-09-13

    Applicant: Shanghai United Imaging Intelligence Co., LTD.

    Inventor： Yikang Liu ,  Shanhui Sun ,  Terrence Chen

    IPC: G06T3/40 ,  G06T7/11

    CPC classification number: G06T3/4046 ,  G06T7/11 ,  G06F3/0482

    Abstract: A magnification system for magnifying an image based on trained neural networks is disclosed. The magnification system receives a first user input associated with a selection of a region of interest (ROI) within an input image of a site and a second user input associated with a first magnification factor of the selected ROI. The first magnification factor is associated with a magnification of the ROI in the input image. The ROI is modified based on an application of a first neural network model on the ROI. The modification of the ROI corresponds to a magnified image that is predicted in accordance with the first magnification factor. A display device is controlled to display the modified ROI.

Patent Agency Ranking