In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers since there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intra-spot intensity. Since this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images, and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore, represents a valuable tool for microarray image analysis.

中文翻译：

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自动识别和定量微阵列图像中的超孔荧光

在最近的涉及NAPPA芯片的研究中，超孔荧光被用作鉴定疾病生物标记物的关键方法，因为有证据表明，与涉及点内强度的统计分析相比，超孔荧光与强抗体反应的相关性更好。由于传统图像分析软件无法很好地量化此功能，因此手动识别和量化超阱荧光的工作非常耗时，并且极易受到评估者之间的差异的影响。一个可以有效且高效地自动化此任务的系统将极大地改善微阵列研究中的数据采集过程，从而加快疾病生物标记物的发现。在这项研究中，我们尝试了不同的机器学习方法以及新颖的启发式算法，用于鉴定微阵列图像中表现出超荧光（环）的斑点，并根据其强度和形态将每个环的等级定为1-5。我们发现用于鉴定环的最终系统的灵敏度在99％特异性下为72％，在92％特异性下为98％。我们的系统在保持高性能的同时，比人类更快地完成了这项任务，因此，它是微阵列图像分析的有价值的工具。