Kayser K, Radziszowski D, Bzdyl P, Sommer R, Kayser G
UICC-TPCC, Institute of Pathology, Charité, University of Berlin, Germany.
Rom J Morphol Embryol. 2006;47(1):21-8.
To describe the theory and develop an automated virtual slide screening system. Theoretical considerations. Tissue-based diagnosis separates into (a) sampling procedure to allocate the slide area containing diagnostic information, and (b) evaluation of diagnosis from the selected area. Nyquist's theorem broadly applied in acoustics, serves to presetting the sampling accuracy. Tissue-based diagnosis relies on two different information systems: (a) texture, and (b) object information. Texture information can be derived by recursive formulas without image segmentation. Object information requires image segmentation and feature extraction. Both algorithms complete another to a "self-learning" classification system.
Non-overlapping compartments of the original virtual slide (image) are chosen at random with predefined error-rate (Nyquist's theorem). The standardized image compartments are subject for texture and object analysis. The recursive formula of texture analysis computes median gray values and local noise distribution. Object analysis includes automated measurements of immunohistochemically stained slides. The computations performed at different magnifications (x 2, x 4.5, x 10, x 20, x 40) are subject to multivariate statistically analysis and diagnosis classification.
A total of 808 lung cancer cases of diagnoses groups: cohort (1) normal lung (318 cases) - cancer (490 cases); cancer subdivided: cohort (2) small cell lung cancer (10 cases) - non-small cell lung cancer (480 cases); non-small cell lung cancer subdivided: cohort (3) squamous cell carcinoma (318 cases) - adenocarcinoma (194 cases) - large cell carcinoma (70 cases) was analyzed. Cohorts (1) and (2) were classified correctly in 100%, cohort (3) in more than 95%. The selected area can be limited to 10% of the original image without increased error rate. A second approach included 233 breast tissue cases (105 normal, 128 breast carcinomas) and 88 lung tissue cases (58 normal, 38 cancer). Texture analysis revealed a correct classification with only 10 training set cases in >92% for both, breast and lung tissue.
The developed system is a fast and reliable procedure to fulfill all requirements for an automated "pre-screening" of virtual slides in tissue-based diagnosis.
描述该理论并开发一个自动化虚拟切片筛选系统。理论考量。基于组织的诊断可分为：(a) 采样程序，用于确定包含诊断信息的切片区域；(b) 对所选区域进行诊断评估。广泛应用于声学的奈奎斯特定理用于预设采样精度。基于组织的诊断依赖于两种不同的信息系统：(a) 纹理信息，以及 (b) 对象信息。纹理信息可通过递归公式得出，无需图像分割。对象信息需要图像分割和特征提取。这两种算法相互补充，构成一个“自学习”分类系统。
以预定义的错误率（奈奎斯特定理）随机选择原始虚拟切片（图像）的非重叠区域。对标准化的图像区域进行纹理和对象分析。纹理分析的递归公式计算中值灰度值和局部噪声分布。对象分析包括对免疫组织化学染色切片的自动测量。在不同放大倍数（x2、x4.5、x10、x20、x40）下进行的计算要经过多变量统计分析和诊断分类。
共分析了808例肺癌诊断组病例：队列(1) 正常肺组织（318例） - 癌组织（490例）；癌组织再细分：队列(2) 小细胞肺癌（10例） - 非小细胞肺癌（480例）；非小细胞肺癌再细分：队列(3) 鳞状细胞癌（318例） - 腺癌（194例） - 大细胞癌（70例）。队列(1) 和(2) 的分类正确率为100%，队列(3) 的正确率超过95%。所选区域可限制在原始图像的10%以内，而不会增加错误率。第二种方法纳入了233例乳腺组织病例（105例正常，128例乳腺癌）和88例肺组织病例（58例正常，38例癌）。纹理分析显示，仅用10个训练集病例，乳腺和肺组织的正确分类率均超过92%。
所开发的系统是一种快速可靠的程序，可满足基于组织的诊断中对虚拟切片进行自动化“预筛选”的所有要求。
