应用深度学习分割光学显微镜下的细胞图像

Application of Deep Learning in Segmentation of Cell Image by Optical Microscope

目的为了在细胞培养过程中便捷地分析细胞的数目和形态。方法本文将深度学习应用到细胞识别中,实现了一种可以通过普通光学显微镜拍照,并直接在培养皿中进行细胞识别计数的方法。结果通过构建U-Net网络结构,并对贴壁细胞和悬浮细胞图像进行标记训练,来实现贴壁细胞和悬浮细胞的分割计数。同时,用该算法绘制细胞生长曲线以及计算抑制剂的抑制率,以验证该算法的实用性。结论应用深度学习分割光学显微镜下的细胞图像是一种可行的方法。

Objective In order to analyze the number and morphology of cells in the process of cell culture conveniently.Methods In this paper, we introduce a cell counting method which can directly count cells in culture dish from images of commercial optical microscope by applying deep learning technology. Results In order to implement cell segmentation and counting, labeling and training is carried out on the image of adherent cells and suspension cells by a U-Net structure network. The cell growth curve is plotted and the inhibition rate of inhibitor is calculated by this algorithm, which shows the practicability of the algorithm. Conclusion It is feasible to do cell segmentation in dish by deep learning method.