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    CROSS-DOMAIN SEGMENTATION WITH UNCERTAINTY-GUIDED CURRICULUM LEARNING
    29.
    发明公开
    CROSS-DOMAIN SEGMENTATION WITH UNCERTAINTY-GUIDED CURRICULUM LEARNING 审中-实审

    公开(公告)号：EP4375948A1

    公开(公告)日：2024-05-29

    申请号：EP22209867.5

    申请日：2022-11-28

    Applicant: Siemens Healthineers AG

    Inventor： ZHANG, Yue ,  SULIMAN, Caius Constantin ,  GHESU, Florin-Cristian ,  LIAO, Rui

    IPC: G06V10/26 ,  G06V10/774 ,  G06V10/82

    CPC classification number: G06V2201/0320220101 ,  G06V10/774 ,  G06V10/26 ,  G06V10/82

    Abstract: Systems and methods for training a machine learning based segmentation network are provided. A set of medical images, each depicting an anatomical object, in a first modality is received. For each respective medical image of the set of medical images, a synthetic image, depicting the anatomical object, in a second modality is generated based on the respective medical image. One or more augmented images are generated based on the synthetic image. One or more segmentations of the anatomical object are performed from the one or more augmented images using a machine learning based reference network. An uncertainty associated with segmenting the anatomical object from the respective medical image is computed based on results of the one or more segmentations. It is determined whether the respective medical image is suitable for training a machine learning based segmentation network based on the uncertainty. The machine learning based segmentation network is trained based on 1) the suitable medical images of the set of medical images and 2) annotations of the anatomical object determined using a machine learning based teacher network.

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