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The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation

机译：识别识别适应性在球形形态分析中识别健全的无标签质量评价的重要性

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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.
机译：由于对药物发育和再生治疗的功能性细胞组分不断增长，因此出现了非侵入性评估其质量的技术。基于图像的球体形态学分析是它们的质量的高通量筛选。然而，由于球形是三维的，它们的图像可以在其表面积中具有差的对比度，因此通过图像处理的总球状识别非常依赖于设计过滤器设定的人以适合其自身的球体轮廓定义。结果，形态学测量的再现性受到过滤器集的性能的关键影响，其波动可能破坏随后的基于形态的分析。虽然从图像处理结果不一致导出的意外失败是用于分析大图像数据的质量筛选的重要问题，但是已经很少地解决了。为了使用形态特征来实现稳健的分析性能，我们研究了滤光机集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，“识别健身偏差（RFD）”，作为定量和全面评估生殖设计的滤波器集合如何使用数据变化，例如复制样本中的变化，在时间课程中的衡量标准样品，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，5000图像的RFD评分可以自动对获得最佳的6细胞类型分类模型（94％精度）自动排列最佳的鲁棒滤波器集。此外，RFD评分反映了最坏的和最佳分类模型之间的差异，分别为相似的球状体，60％和89％的精度。除了RFD评分外，我们发现，使用形态学特征的时间过程可以增强球状识别的波动，导致鲁棒形态分析。

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