针对动态观察活体细胞形态的数字全息显微应用, 提出了一种结合划线拟合和深度学习的自动相位像差补偿方法.首先在全息面提取中心十字线上的物光场相位值, 通过二次多项式数值拟合构建相位掩模完成相位像差初步补偿.然后在成像面运用卷积神经网络生成二值化图像掩膜, 提取物光场中无物体区域的相位值, 再次通过高阶多项式进行数字拟合构建相位掩模完成相位像差精确补偿.最后得到无相位像差的再现相位像.该方法通过在全息面划线取值和数字拟合有效补偿物光中的主要相位像差, 降低了成像面物光场的图像轮廓复杂性, 利用有限的训练数据集获得能够准确建立图像分割的深度学习模型, 从而实现了准确的无需人工干预的数字全息显微自动相位像差补偿.基于离轴数字全息显微成像系统对多种具有不同形态特征的活体细胞开展动态观察实验, 并进一步应用该方法进行子宫内膜癌细胞抗药性筛选.结果表明该方法可以很好地用于动态显微观察, 从而为生物学细胞动态研究提供实验依据.

An automatic aberration compensation method is proposed by combing phase fitting and deep learning in digital holographic microscopy and applications to dynamic observation of living cell morphology. Firstly, in the holography recording plane, the preliminary aberration compensation is implemented by using a digital phase mask, whose quadratic polynomial fitting computation is based on the extraction of reconstructed phase value along central cross line profiles in the field of view. Then, in the holographic imaging plane, the final aberration compensation is completed by using higher-order polynomial digital phase mask that computed with the phase data in the object free region, which is defined by the convolutional neural network. Thus, the phase image of object is correctly and accurately reconstructed. Thanks to the preliminary aberration compensation in the holography recording plane, the complexity of reconstruction in the holographic imaging plane is effectively reduced before the convolutional neural network training. Therefore, a stable deep learning model for phase image segmentation can be obtained on basis of limited data set and the compensation of phase aberration can be achieved without any manual intervention. The experiments are demonstrated by observing the several kinds of living cells, which have different morphological characteristics, with the off-axis digital holographic microscopy. Furthermore, the proposed method is applied to screen the drug resistance of endometrial cancer cells. These experimental results verify the proposed method and show that it can be used to dynamic microscopic observation in biological cell research.