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    Anonymisation of medical patient images using an atlas
    11.
    发明授权
    Anonymisation of medical patient images using an atlas 有权

    公开(公告)号：US11593519B2

    公开(公告)日：2023-02-28

    申请号：US17225000

    申请日：2021-04-07

    Applicant: Brainlab AG

    Inventor： Andreas Blumhofer ,  Jens Schmaler

    IPC: G06K9/00 ,  G06F21/62 ,  G16H30/40 ,  G06T5/00

    Abstract: Disclosed is a computer-implemented method for generating an anonymized medical image of an anatomical body part of a patient, a corresponding computer program, a program storage medium storing such a program and a computer for executing the program, as well as a medical system comprising an electronic data storage device and the aforementioned computer. The disclosed method encompasses establishing a mapping from a patient image onto an atlas, changing that mapping, and applying the inverse of the changed mapping to the atlas in order to transform image content from the atlas to the patient image in order to achieve a deformed and thereby anonymised appearance of the patient image.

    Correcting segmentation of medical images using a statistical analysis of historic corrections
    14.
    发明授权
    Correcting segmentation of medical images using a statistical analysis of historic corrections 有权

    公开(公告)号：US11861846B2

    公开(公告)日：2024-01-02

    申请号：US17281493

    申请日：2019-12-20

    Applicant: Brainlab AG

    Inventor： Stefan Vilsmeier ,  Andreas Blumhofer ,  Jens Schmaler

    IPC: G06T7/143 ,  G06T7/174 ,  G06T7/00 ,  G06T7/11 ,  G06N3/08 ,  G06N20/20

    CPC classification number: G06T7/143 ,  G06T7/0012 ,  G06T7/174 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20092 ,  G06T2207/20128 ,  G06T2207/30004

    Abstract: Disclosed is a computer-implemented methods of determining distributions of corrections for correcting the segmentation of medical image data, determining corrections for correcting the segmentation of medical image data, training a learning algorithm for determining a segmentation of a digital medical image, and determining a relation between an image representation of the anatomical body part in an individual medical image and a label to be associated with the image representation of the anatomical body part in the individual medical image using the trained machine learning algorithm. The methods encompass reading a plurality of corrections to image segmentations, wherein the corrections themselves may have been manually generated, transforming these corrections into a reference system which is not patient-specific such as an atlas reference system, conducting a statistical analysis of the correction, and applying the re-transformed result of the statistical analysis to patient images. The result of the statistical analysis may also be used to appropriately train a machine learning algorithm for automatic segmentation of patient images. The application of such a trained machine learning algorithm is also part of this disclosure.

    DETERMINING IMAGE SIMILARITY BY ANALYSING REGISTRATIONS
    16.
    发明申请
    DETERMINING IMAGE SIMILARITY BY ANALYSING REGISTRATIONS 有权

    公开(公告)号：US20230087494A1

    公开(公告)日：2023-03-23

    申请号：US17801464

    申请日：2021-03-26

    Applicant: Brainlab AG

    Inventor： Stefan Vilsmeier ,  Jens Schmaler

    IPC: G06V10/74 ,  G06T7/33 ,  G06V10/774 ,  G16H30/20 ,  G16H10/60

    Abstract: Disclosed are computer-implemented methods which encompass determining whether two medical images were taken of the same patient. In a first aspect, this is done by analysing a registration of the two images with one another. The registration may be a direct registration between the two images or an indirect registration, for example via an atlas to which each image is registered. In other aspects, a machine learning algorithm is trained on the basis of image registrations to determine whether the two images were taken of the same patient. The disclosed methods serve the purpose of being able to group medical images together which were taken of the same patient without having to provide or otherwise process data about the identity of the patient.

    MEDICAL IMAGE ANALYSIS USING MACHINE LEARNING AND AN ANATOMICAL VECTOR
    17.
    发明申请
    MEDICAL IMAGE ANALYSIS USING MACHINE LEARNING AND AN ANATOMICAL VECTOR 有权

    公开(公告)号：US20230046321A1

    公开(公告)日：2023-02-16

    申请号：US17783851

    申请日：2020-12-16

    Applicant: Brainlab AG

    Inventor： Stefan Vilsmeier ,  Jens Schmaler

    IPC: G06V10/774 ,  G06V10/82 ,  G06V20/70 ,  G06T7/70 ,  G06V20/69 ,  G06V10/26 ,  G06V10/764

    Abstract: Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and/or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

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