Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the “recognition fitness deviation (RFD),” as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.

由于对人类细胞球体作为药物开发和再生治疗的功能性细胞成分的需求不断增长，非侵入性评估其质量的技术已经出现。基于图像的球体形态分析可以对其质量进行高通量筛选。然而，由于球体是三维的，其图像在其表面区域的对比度较差，因此通过图像处理进行的总体球体识别在很大程度上取决于设计滤波器组以适合他们自己的球体轮廓定义的人。因此，形态测量的再现性受到滤波器组性能的严重影响，其波动会扰乱后续基于形态的分析。尽管图像处理结果不一致导致的意外故障是分析大图像数据进行质量筛选的关键问题，但很少得到解决。为了使用形态特征实现稳健的分析性能，我们研究了滤波器组的再现性对各种类型的球体数据的影响。我们提出了一个新的评分指数，即“识别适应度偏差（RFD）”，作为定量和全面评估设计的过滤器组在时间过程中如何有效地处理数据变化（例如重复样本的变化）的衡量标准样本以及不同类型的细胞（总共六种正常或癌细胞类型）。我们的结果表明，对 5000 张图像进行 RFD 评分可以自动对最佳鲁棒过滤器集进行排名，以获得最佳 6 细胞类型分类模型（准确度为 94%）。 此外，RFD 分数反映了形态相似球体的最差和最佳分类模型之间的差异，准确率分别为 60% 和 89%。除了 RFD 评分之外，我们发现使用形态特征的时间过程可以增强球体识别的波动，从而实现稳健的形态分析。