Becker R L
Armed Forces Institute of Pathology, Department of Cellular Pathology, Washington, DC 20306-6000.
J Cell Biochem Suppl. 1993;17G:199-204. doi: 10.1002/jcb.240531137.
Standardization and quality control of quantitative microscopy techniques are distinct but related concerns. The first deals with the great variety of quantitative methods, measured features, and even response variables used in investigation of biological or clinical processes. The latter deals with reproducibility of results from those investigations across time and test performance sites. Though distinct, efforts for standardization and quality control are inherently interactive. Consensus on standard methods, instrumentation, and data analysis is hard to achieve in fields developing as rapidly as quantitative microscopy. Consensus is possible, however, on the issues that affect test performance and interpretation. For example, issues of specimen type, fixation, processing, and staining affect image cytometry just as they do flow cytometry. Raw data acquisition issues include area sampling rules and fidelity of optical and sensor systems (light wavelength, glare/stray light, lens aberrations, numerical aperture, depth of focus, scan precision, pixel spacing and depth, sensor linearity, and stability). Intermediate data issues are primarily related to image foreground/background segmentation techniques--automated versus manual, object-specific versus field-based. Data reduction and interpretation procedures also provide many roads for divergence from uniformity. Each of these issues must be considered in terms of its effect on comparability and utility of quantitative microscopy results. Quality control for quantitative microscopy is as important as standardization for its use in research programs and with clinical specimens. The sine qua non of quality control is comparison of experimental results against a known "correct" value to estimate accuracy, and against other experimental results to estimate precision.(ABSTRACT TRUNCATED AT 250 WORDS)
定量显微镜技术的标准化和质量控制是不同但相关的问题。前者涉及用于生物学或临床过程研究的大量定量方法、测量特征,甚至响应变量。后者涉及这些研究结果在不同时间和测试执行地点的可重复性。尽管有所不同,但标准化和质量控制的努力本质上是相互作用的。在像定量显微镜这样快速发展的领域,很难就标准方法、仪器和数据分析达成共识。然而,在影响测试性能和解释的问题上达成共识是可能的。例如,标本类型、固定、处理和染色等问题对图像细胞术的影响与对流式细胞术的影响一样。原始数据采集问题包括面积采样规则以及光学和传感器系统的保真度(光波长、眩光/杂散光、透镜像差、数值孔径、焦深、扫描精度、像素间距和深度、传感器线性度和稳定性)。中间数据问题主要与图像前景/背景分割技术有关——自动与手动、特定对象与基于视野。数据简化和解释程序也为偏离一致性提供了多种途径。必须从这些问题对定量显微镜结果的可比性和实用性的影响方面来考虑每一个问题。定量显微镜的质量控制与其在研究项目和临床标本中的标准化一样重要。质量控制的必要条件是将实验结果与已知的“正确”值进行比较以估计准确性,并与其他实验结果进行比较以估计精密度。(摘要截短于250字)