SYSTEM AND METHOD FOR ASSESSING OBSTETRIC WELLBEING

    公开(公告)号:US20200060657A1

    公开(公告)日:2020-02-27

    申请号:US16109736

    申请日:2018-08-22

    Abstract: A system includes a memory unit comprising a classifier network and a detector network. The classifier network is configured to perform a classification of a scan image among maternal images. The detector network is configured to determine a placenta condition in the scan image. The system further includes a data acquisition unit communicatively coupled to an ultrasound scanner and configured to receive maternal images from a maternal scanning procedure. The system also includes an image processing unit communicatively coupled to the memory unit and the data acquisition unit and configured to select a sagittal image from the maternal images using the classifier network. The image processing unit is further configured to determine a placenta condition based on the selected sagittal image using the detector network. The image processing unit is also configured to provide a recommendation to a medical professional based on the placenta condition.

    Automatic estimation of anatomical extents
    6.
    发明授权
    Automatic estimation of anatomical extents 有权
    自动估计解剖范围

    公开(公告)号:US09355454B2

    公开(公告)日:2016-05-31

    申请号:US13852781

    申请日:2013-03-28

    Abstract: A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit.

    Abstract translation: 公开了一种用于处理诸如定位器或侦察图像数据的图像数据的分级多对象主动外观模型(AAM)框架。 根据这种方法,可以使用模型(例如,模型金字塔)的分层布置,其中使用编码跨多个同位置结构的关系的全局或父模型来获得初始粗匹配。 儿童子模型的后续处理增加了整体配合的更多细节和灵活性。

    Material segmentation in image volumes

    公开(公告)号:US10438350B2

    公开(公告)日:2019-10-08

    申请号:US15634657

    申请日:2017-06-27

    Abstract: The present approach relates, in some aspects, to a multi-level and a multi-channel frame work for segmentation using model-based or “shallow” classification (i.e. learning processes such as linear regression, clustering, support vector machines, and so forth) followed by deep learning. This framework starts with a very low resolution version of the multi-channel data and constructs an shallow classifier with simple features to generate a coarser level tissue mask that in turn is used to crop patches from the high-resolution volume. The cropped volume is then processed using the trained convolution network to perform a deep learning based segmentation within the slices.

    SYSTEM AND METHOD FOR RECALIBRATING A MONOCHROMATIC IMAGE
    8.
    发明申请
    SYSTEM AND METHOD FOR RECALIBRATING A MONOCHROMATIC IMAGE 有权
    用于记录单色图像的系统和方法

    公开(公告)号:US20160171694A1

    公开(公告)日:2016-06-16

    申请号:US14964052

    申请日:2015-12-09

    Abstract: A method includes receiving a monochromatic image comprising a head of a subject from a Computed Tomography (CT) scanner and detecting a petrous bone of the head in the monochromatic image. The method further includes determining a linear attenuation coefficient of at least one petrous voxel representing the petrous bone and calculating a mass attenuation coefficient of the petrous voxel based on the linear attenuation coefficient and a density of the petrous bone. The method also includes computing a monochromatic energy level of the monochromatic image based on the mass attenuation coefficient of the petrous voxel and recalibrating the monochromatic image corresponding to the computed monochromatic energy level to the desired monochromatic energy level.

    Abstract translation: 一种方法包括从计算机断层摄影(CT)扫描器接收包括受试者的头部的单色图像,并检测单色图像中头部的石骨。 该方法还包括确定表示石质骨的至少一个石质体素的线性衰减系数,并且基于线性衰减系数和石蜡的密度来计算石质体素的质量衰减系数。 该方法还包括基于石墨体素的质量衰减系数来计算单色图像的单色能级,并将对应于所计算的单色能级的单色图像重新校准到期望的单色能级。

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