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    2D-3D IMAGE REGISTRATION METHOD AND MEDICAL OPERATING ROBOT SYSTEM FOR PERFORMING THE SAME
    61.
    发明公开
    2D-3D IMAGE REGISTRATION METHOD AND MEDICAL OPERATING ROBOT SYSTEM FOR PERFORMING THE SAME 审中-实审

    公开(公告)号：EP4404140A2

    公开(公告)日：2024-07-24

    申请号：EP23207139.9

    申请日：2023-10-31

    申请人： Curexo, Inc.

    发明人： WOO, Dong Gi ,  HWANG, Sung Teac

    IPC分类号： G06T7/33 ,  A61B34/30

    CPC分类号： A61B34/30 ,  G06T7/33 ,  G06T2207/3001220130101 ,  G06T2207/1008120130101 ,  A61B34/25 ,  A61B2034/10520160201 ,  G06N20/00 ,  A61B2090/398320160201 ,  A61B2034/205520160201

    摘要： Disclosed are an image registration method and apparatus, a medical operating robot system using the same, and a computer program medium. The image registration method includes: acquiring a 3D image of a patient's surgical site from a 3D imaging apparatus before an operation; extracting digitally reconstructed radiograph (DRR) images in an anterior-posterior (AP) direction and a lateral-lateral (LL) direction from the 3D image; acquiring 2D images for an AP image and an LL image of the patient's surgical site from a 2D imaging apparatus during an operation; determining a first rotation angle between a reference position and a predetermined first reference position of the patient's surgical site corresponding to the first reference position of the AP image or LL image, based on a first rotation axis passing through a predetermined first origin and parallel to a cross product vector of first normal vectors for planes of the AP image and the LL image, from a geospatial relationship between a source and a detector with respect to the DRR image; determining a second rotation angle between the reference position and the second reference position of the AP image or LL image corresponding to the reference position, based on a second rotation axis passing through a predetermined second origin and parallel to a cross product vector of second normal vectors for planes of the AP image and the LL image, from a geospatial relationship between a source and a detector with respect to the 2D image; and determining a transformation relationship between the 2D image and the DRR image based on the first and second rotation angles, from the geospatial relationships between the sources and the detectors of the DRR and 2D images.

    METHOD FOR TRAINING A SYSTEM MODEL, SELECTING A CONTROLLER, SYSTEM, COMPUTER-SYSTEM
    66.
    发明公开
    METHOD FOR TRAINING A SYSTEM MODEL, SELECTING A CONTROLLER, SYSTEM, COMPUTER-SYSTEM 审中-公开

    公开(公告)号：EP4400975A1

    公开(公告)日：2024-07-17

    申请号：EP23150926.6

    申请日：2023-01-10

    申请人： Siemens Industry Software NV

    发明人： Tong, Son

    IPC分类号： G06F11/36 ,  G06F30/20 ,  G05B13/04 ,  B60W50/00

    CPC分类号： G05B13/04 ,  G06N20/00 ,  G06F11/3684 ,  B60W50/00 ,  G06F30/20

    摘要： The invention relates to a computer-implemented method for training a predefined system model (MDL) of a parametrized system (SYS), wherein scenarios (SCN) are a parameter set defining a system (SYS) state and/or system (SYS) operation, the method comprising:
    (a) providing said system model (MDL),
    (b) providing a first set (IST) of scenarios (SCN),
    (c) selecting a sub-set (SST) of scenarios (SCN),
    (d) acquiring system (SYS) test data (TDT) for the sub-set (SST) of scenarios (SCN),
    (e) generating system model (MDL) data for the sub-set (SST) of scenarios (SCN),
    (f) determining a modeling error (MER) of the system model (MDL) by comparing the test data (TDT) with the system model (MDL) data.
    To reduce the model training complexity the method according to the invention proposes the additional steps:
    (g) generating and/or training an error prediction model (EPM) on basis of the modeling errors (MER) determined,
    (h) determining an error prediction (ERP) by the error prediction model (EPM) for the first set (IST) of scenarios (SCN),
    (i) selecting a certain portion of the first set (IST) of scenarios (SCN) with the highest error prediction (ERP) determined by the error prediction model (EPM) as top error scenarios (TES),
    (j) acquiring system (SYS) test data (TDT) for the selected top error scenarios (TES),
    (k) training said system model (MDL) with the acquired system (SYS) test data (TDT) for the selected top error scenarios (TES).