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Tissue microarray design and construction for scientific, industrial and diagnostic use

机译:用于科学,工业和诊断用途的组织微阵列设计和构建

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Context:In 2013 the high throughput technology known as Tissue Micro Array (TMA) will be fifteen years old. Its elements (design, construction and analysis) are intuitive and the core histopathology technique is unsophisticated, which may be a reason why has eluded a rigorous scientific scrutiny. The source of errors, particularly in specimen identification and how to control for it is unreported. Formal validation of the accuracy of segmenting (also known as de-arraying) hundreds of samples, pairing with the sample data is lacking.Aims:We wanted to address these issues in order to bring the technique to recognized standards of quality in TMA use for research, diagnostics and industrial purposes.Results:We systematically addressed the sources of error and used barcode-driven data input throughout the whole process including matching the design with a TMA virtual image and segmenting that image back to individual cases, together with the associated data. In addition we demonstrate on mathematical grounds that a TMA design, when superimposed onto the corresponding whole slide image, validates on each and every sample the correspondence between the image and patient's data.Conclusions:High throughput use of the TMA technology is a safe and efficient method for research, diagnosis and industrial use if all sources of errors are identified and addressed.
机译:背景:2013年,被称为组织微阵列(TMA)的高通量技术将使用15年。它的元素(设计,构造和分析)直观,组织病理学的核心技术也不复杂,这可能是未能进行严格科学审查的原因。尚未报告错误的来源,尤其是在标本识别以及如何控制错误方面。缺乏对数百个样品进行分割(也称为去阵列)的准确性的正式验证,缺乏与样品数据配对的目的:我们想解决这些问题,以便使该技术达到公认的TMA使用质量标准结果:我们在整个过程中系统地解决了错误源,并使用了条形码驱动的数据输入,包括将设计与TMA虚拟图像进行匹配并将该图像分割回各个案例以及相关数据。此外,我们以数学为依据证明,将TMA设计叠加到相应的完整载玻片图像上时,可以在每个样本上验证图像与患者数据之间的对应关系。结论:TMA技术的高通量使用是安全有效的确定并解决所有错误源的研究,诊断和工业使用方法。

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