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    • 6. 发明公开
    • SYSTEMS AND METHODS FOR CALCULATING IMMUNE SCORES
    • 用于计算免疫比例的系统和方法
    • EP3195191A2
    • 2017-07-26
    • EP15767102.5
    • 2015-09-03
    • Ventana Medical Systems, Inc.
    • BARNES, MichaelCHEN, TingCHEFD'HOTEL, ChristopheTUBBS, AlisaASCIERTO, Paolo A.
    • G06K9/00G01N33/50G06T7/00
    • Embodiments disclosed herein are directed, among other things, to imaging systems, methods, and apparatuses for automatically identifying fields of view (FOVs) for regions in an image encompassing tumor are disclosed. In embodiments and in further aspects of the present invention, a computer-implemented method is disclosed for a tumor region based immune score computation. The method, in accordance with the present invention, involves identifying regions, for example, tumor areas or regions around a tumor area, partitioning a whole slide image or portion of a whole slide image into multiple regions related to the tumor, selecting FOVs within each identified region, and computing a number of cells present in each FOV. An immune score and/or immune-related score may be generated based on the cells counted in each FOV.
    • 公开了用于自动识别包含黑素瘤的图像中的区域的视野(FOV)的成像系统,方法和设备。 在本发明的实施例和其他方面中,公开了一种用于基于肿瘤区域的免疫分数计算的计算机实现的方法。 根据本发明,该方法涉及识别区域,例如肿瘤区域或肿瘤区域周围的区域,将整个载玻片图像或整个载玻片图像的一部分分成与肿瘤相关的多个区域,在每个区域内选择FOV 识别的区域,并计算每个FOV中存在的细胞的数量。 基于在每个FOV中计数的细胞生成免疫分数和/或免疫相关分数。
    • 7. 发明公开
    • ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION
    • 自适应KLASSIFIZIERUNG VON GEWEBESEGMENTIERUNG MITVOLLSTÄNDIGENOBJEKTTRGERN
    • EP3100205A1
    • 2016-12-07
    • EP15703003.2
    • 2015-01-23
    • Ventana Medical Systems, Inc.
    • BREDNO, JoergCHUKKA, SrinivasCHEN, TingCHEFD'HOTEL, ChristopheNGUYEN, Kien
    • G06K9/00G06K9/46G06K9/62G06K9/66
    • G06K9/00147G06K9/4642G06K9/4652G06K9/623G06K9/66G06T7/0012G06T7/11G06T7/40G06T7/74G06T2207/20081G06T2207/30024
    • A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.
    • 使用自适应分类将生物样本的图像分割成不同类型的组织区域的方法。 通过以下步骤执行分割:首先,从全滑动(WS)图像上采样的点格网(GP)的邻域提取特征,并将其分类成不同的组织类型。 其次,使用预先构建的训练数据库对WS图像中的一些或全部GP进行分类,并且生成GP的分类置信度得分,进行自适应分类过程。 具有高置信度分数的分类GP用于生成适应性训练数据库,然后将其用于重新分类低置信度GP。 该方法的动机是,组织外观的强烈变化使得分类问题更具挑战性,而当训练和测试数据来自同一幻灯片时,可以获得良好的分类结果。