Shirai Kazuhide, Kato Hirohito, Imai Yuta, Shibuta Mayu, Kanie Kei, Kato Ryuji
Graduate School of Pharmaceutical Sciences, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8601, Japan.
Mathematical Sciences Research Laboratory, Research & Development Division, Nikon Corporation, Yokohama Plant, 471, Nagaodai-cho, Sakae-ku, Yokohama-city, Kanagawa 244-8533, Japan.
Regen Ther. 2020 May 14;14:205-214. doi: 10.1016/j.reth.2020.02.004. eCollection 2020 Jun.
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应运而生。基于图像的球体形态分析能够对其质量进行高通量筛选。然而，由于球体是三维的，其图像在表面积上的对比度可能较差，因此通过图像处理进行的总球体识别在很大程度上依赖于设计滤波器组以符合其自身球体轮廓定义的人员。结果，形态测量的可重复性受到滤波器组性能的严重影响，其波动可能会干扰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失败对于分析用于质量筛选的大型图像数据是一个关键问题，但很少有人解决。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可重复性对各种类型球体数据的影响。我们提出了一种新的评分指标，即“识别适应度偏差（RFD）”，作为一种定量和全面评估设计的滤波器组在面对数据变化（如重复样本、时间进程样本和不同类型细胞（总共六种正常或癌细胞类型）中的变化）时能够多可重复地工作的度量。我们的结果表明，对5000张图像进行RFD评分可以自动对最佳稳健滤波器组进行排名，以获得最佳的6细胞类型分类模型（准确率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评分外，我们还发现使用形态特征的时间进程可以增强球体识别中的波动，从而实现稳健的形态分析。
