Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet.
Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet.
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迈向自动化的虚拟幻灯片筛选：在互联网中实施的基于自动化组织的虚拟诊断的理论考虑和实际经验。
DOI：
10.1186/1746-1596-1-10
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发表时间：
2006-06-10
影响因子：
2.6
通讯作者：
Kayser, Gian
中科院分区：
文献类型：
--
作者：
Kayser, Klaus;Radziszowski, Dominik;Bzdyl, Piotr;Sommer, Rainer;Kayser, Gian
开发和实施一种自动虚拟幻灯片筛选系统，以区分正常组织学发现和几种基于组织的粗糙诊断。虚拟幻灯片技术必须处理和传输GB字节大小的图像。基于组织的诊断性能可以分为a)分配包含最重要诊断信息的滑动区域的采样程序，以及b)从所选区域中存在的信息获得的诊断的评价。在声学中广泛应用的奈奎斯特定理也可以作为图像信息分析的质量保证，特别是预设采样的精度。基于纹理的诊断可以用递归公式执行，不需要详细的分割过程。然后，获得的结果将被转移到“自学习”识别系统中，该系统可以根据图像参数（如亮度、阴影或对比度）的变化进行自我调整。原始虚拟幻灯片（图像）的非重叠区域将根据奈奎斯特定理（预定义错误率）随机选择。隔间将通过局部过滤操作标准化，并进行纹理分析。纹理分析是在计算灰度中值和局部噪声分布的递归公式的基础上进行的。计算将在调整到最常用物镜（*2,*4.5,*10,*20,*40）的不同倍率下进行。所得数据按层次顺序进行统计分析，并与诊断的临床意义相关。该系统已在总共896例肺癌病例中进行了测试，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。该系统对队列(1)和队列(2)的所有诊断的分类正确率为100%，队列(3)的分类正确率为95%以上。所选区域的百分比可以限制为原始图像的10%，而不会增加错误率。开发的系统是一种快速可靠的程序，可以满足肺部病理虚拟切片自动“预筛选”的所有要求。
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.
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影响因子：
7.3
作者：
Leong, FJWM;McGee, J
通讯作者：
McGee, J
影响因子：
3.7
作者：
ZAHN, CT
通讯作者：
ZAHN, CT
影响因子：
2
作者：
GUNDERSEN, HJG
通讯作者：
GUNDERSEN, HJG
影响因子：
6.2
作者：
BARTELS, PH
通讯作者：
BARTELS, PH
影响因子：
23.4
作者：
Bartels, PH;Montironi, R;Bartels, HG
通讯作者：
Bartels, HG