Kayser Klaus, Radziszowski Dominik, Bzdyl Piotr, Sommer Rainer, Kayser Gian
UICC-TPCC, Charite, University of Berlin, Berlin, Germany.
Diagn Pathol. 2006 Jun 10;1:10. doi: 10.1186/1746-1596-1-10.
To develop and implement an automated virtual slide screening system that distinguishes normal histological findings and several tissue--based crude (texture-based) diagnoses.
Virtual slide technology has to handle and transfer images of GB Bytes in size. The performance of tissue based diagnosis can be separated into a) a sampling procedure to allocate the slide area containing the most significant diagnostic information, and b) the evaluation of the diagnosis obtained from the information present in the selected area. Nyquist's theorem that is broadly applied in acoustics, can also serve for quality assurance in image information analysis, especially to preset the accuracy of sampling. Texture-based diagnosis can be performed with recursive formulas that do not require a detailed segmentation procedure. The obtained results will then be transferred into a "self-learning" discrimination system that adjusts itself to changes of image parameters such as brightness, shading, or contrast.
Non-overlapping compartments of the original virtual slide (image) will be chosen at random and according to Nyquist's theorem (predefined error-rate). The compartments will be standardized by local filter operations, and are subject for texture analysis. The texture analysis is performed on the basis of a recursive formula that computes the median gray value and the local noise distribution. The computations will be performed at different magnifications that are adjusted to the most frequently used objectives (*2, *4.5, *10, *20, *40). The obtained data are statistically analyzed in a hierarchical sequence, and in relation to the clinical significance of the diagnosis.
The system has been tested with a total of 896 lung cancer cases that include the diagnoses groups: cohort (1) normal lung--cancer; cancer subdivided: cohort (2) small cell lung cancer--non small cell lung cancer; non small cell lung cancer subdivided: cohort (3) squamous cell carcinoma--adenocarcinoma--large cell carcinoma. The system can classify all diagnoses of the cohorts (1) and (2) correctly in 100%, those of cohort (3) in more than 95%. The percentage of the selected area can be limited to only 10% of the original image without any increased error rate.
The developed system is a fast and reliable procedure to fulfill all requirements for an automated "pre-screening" of virtual slides in lung pathology.
开发并实施一种自动虚拟切片筛查系统，以区分正常组织学表现和几种基于组织的粗略（基于纹理）诊断。
虚拟切片技术必须处理和传输大小为千兆字节的图像。基于组织的诊断性能可分为：a）一种采样程序，用于分配包含最重要诊断信息的切片区域；b）对从所选区域中的信息获得的诊断进行评估。广泛应用于声学的奈奎斯特定理，也可用于图像信息分析中的质量保证，尤其是预设采样精度。基于纹理的诊断可通过无需详细分割程序的递归公式来执行。然后将获得的结果传输到一个“自学习”判别系统中，该系统会根据图像参数（如亮度、阴影或对比度）的变化进行自我调整。
将根据奈奎斯特定理（预定义错误率）随机选择原始虚拟切片（图像）的非重叠区域。这些区域将通过局部滤波操作进行标准化，并进行纹理分析。纹理分析基于一个计算中值灰度值和局部噪声分布的递归公式进行。计算将在不同放大倍数下进行，这些放大倍数与最常用的物镜（*2、*4.5、*10、*20、*40）相适配。所获得的数据将按照分层顺序并结合诊断的临床意义进行统计分析。
该系统已对总共896例肺癌病例进行了测试，这些病例包括诊断组：队列（1）正常肺——癌；癌症细分：队列（2）小细胞肺癌——非小细胞肺癌；非小细胞肺癌细分：队列（3）鳞状细胞癌——腺癌——大细胞癌。该系统能100%正确分类队列（1）和（2）的所有诊断，队列（3）的诊断正确率超过95%。所选区域的百分比可限制在原始图像的仅10%，而不会增加错误率。
所开发的系统是一种快速且可靠的程序，可满足肺部病理学中虚拟切片自动“预筛查”的所有要求。
