A multi-resolution textural approach to diagnostic neuropathology reporting
A multi-resolution textural approach to diagnostic neuropathology reporting
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DOI：
10.1007/s11060-015-1872-4
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发表时间：
2015-09-01
影响因子：
3.9
通讯作者：
Gurcan, Metin N.
中科院分区：
文献类型：
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作者：
Fauzi, Mohammad Faizal Ahmad;Gokozan, Hamza Numan;Gurcan, Metin N.
我们提出了一个计算机辅助诊断工作流程，重点是神经病理学的两个诊断分支点（术中会诊和肿瘤活检标本中的p53状态），通过离散小波帧分解的纹理分析。对于术中会诊，我们的方法能够根据胶质突的成像特征从细胞学准备的非核区域提取纹理特征，从而区分胶质母细胞瘤和转移性癌，胶质突表现为各向异性的细线性结构。对于转移，这些组织在外观上是均匀的，因此合适和可提取的纹理特征区分了两种组织类型。53张图像（29张胶质母细胞瘤和24张转移瘤）的实验结果表明，胶质母细胞瘤的平均准确率高达89.7%，转移率为87.5%，总体准确率为88.7%。为了解释p53，我们通过将染色强度分为强、中、弱和阴性亚类来检测和分类p53状态。我们通过开发一种新的自适应阈值检测来实现这一目标，这是一种基于加权颜色和强度的两步规则，用于正染色核和负染色核的分类，然后是纹理分类，将正染色核分为强、中等和弱强度亚类。我们的检测方法能够正确地定位和区分四种类型的细胞，平均精度为85%，平均灵敏度为88%。另一方面，这些分类方法对阳性和阴性细胞的分类准确率为81%，对阳性细胞进一步分类为三个强度组的准确率为60%，这与神经病理学家的标记相当。
We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments 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. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.