A rapid segmentation method of cell boundary for developing embryos using machine learning with a personal computer

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.