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首页> 外文会议>Conference on digital pathology >Classification of Glioblastoma and Metastasis for Neuropathology Intraoperative Diagnosis: A Multi-resolution Textural Approach to Model the Background
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Classification of Glioblastoma and Metastasis for Neuropathology Intraoperative Diagnosis: A Multi-resolution Textural Approach to Model the Background

机译：胶质母细胞瘤的分类和神经病理学术中转移的诊断：多分辨率纹理方法为背景建模

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Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers.
机译：脑癌手术需要通过神经病理学进行术中咨询，以指导有关肿瘤进行大体全切除的程度的手术决策。在这种情况下，胶质母细胞瘤和转移性癌症之间的鉴别诊断具有挑战性，因为必须在短时间内（通常为30分钟）在手术期间做出决定。我们提出了一种基于从细胞学制剂的非细胞核区域提取纹理特征来分类胶质母细胞瘤与转移性癌症的方法。对于胶质母细胞瘤，这些感兴趣的区域充满了核之间的神经胶质突，看起来是各向异性的细线状结构。对于转移，这些区域对应于更均匀的外观，因此可以从这些区域提取合适的纹理特征以区分两种组织类型。在我们的工作中，我们使用离散小波帧来表征基础纹理，这是由于其在建模基础纹理时具有多分辨率功能。通过将我们视觉上有意义的分解分割算法适应此问题，在对核区域进行分割之后，主要在非核区域中进行纹理表征。然后使用k近邻法将特征分类为胶质母细胞瘤或转移癌类别。对53幅图像（29个胶质母细胞瘤和24个转移灶）进行的实验得出，胶质母细胞瘤的平均准确率高达89.7％，转移的准确率高达87.5％，总体准确率高达88.7％。正在进行进一步的研究，以将核区域特征纳入扩展数据集的分类中，以及将分类扩展到更多类型的癌症。

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