Department of Life Sciences, Graduate School of Arts and Sciences, the University of Tokyo, Tokyo, Japan.
Dev Growth Differ. 2021 Oct;63(8):406-416. doi: 10.1111/dgd.12747. Epub 2021 Sep 20.
Cell segmentation is crucial in the study of morphogenesis in developing embryos, but it had been limited in its accuracy until machine learning methods for image segmentation like U-Net. However, these methods take too much time. In this study, we provide a rapid method for cell segmentation using machine learning with a personal computer, termed Cell Segmentator using Machine Learning (CSML). CSML took four seconds per image with a personal computer for segmentation on average, much less than time to obtain an image. We observed that F-value of segmentation by CSML was around 0.97, showing better performance than state-of-the-art methods like RACE and watershed in assessing the segmentation of Xenopus ectodermal cells. CSML also showed slightly better performance and faster than other machine learning-based methods such as U-Net. CSML required only one whole embryo image for training a Fully Convolutional Network classifier and only two parameters. To validate its accuracy, we compared CSML to other methods in assessing several indicators of cell shape. We also examined the generality of this approach by measuring its performance of segmentation of independent images. Our data demonstrate the superiority of CSML, and we expect this application to improve efficiency in cell shape studies.
细胞分割在研究胚胎发育中的形态发生中至关重要，但直到像 U-Net 这样的用于图像分割的机器学习方法出现之前，它的准确性一直受到限制。然而，这些方法需要太多的时间。在这项研究中，我们使用机器学习为个人计算机提供了一种快速的细胞分割方法，称为使用机器学习的细胞分割器（CSML）。CSML 平均每个图像需要四秒钟进行分割，比获取图像的时间要少得多。我们观察到 CSML 分割的 F 值约为 0.97，在评估非洲爪蟾外胚层细胞的分割方面，其性能优于 RACE 和分水岭等最先进的方法。CSML 也表现出比其他基于机器学习的方法（如 U-Net）更好的性能和更快的速度。CSML 仅需一张完整的胚胎图像即可训练全卷积网络分类器，并且仅需两个参数。为了验证其准确性，我们在评估细胞形状的几个指标方面将 CSML 与其他方法进行了比较。我们还通过测量其对独立图像的分割性能来检验这种方法的通用性。我们的数据证明了 CSML 的优越性，我们期望这种应用能够提高细胞形状研究的效率。
