Cell dynamic morphology analysis by deep convolutional features

Computational analysis of cell dynamic morphology in time-lapse image is a challenging task in biomedical research. Inspired by the recent success of deep learning, we investigate the possibility to apply a deep neural network to cell dynamic morphology analysis in this paper. Specifically, a contour spectrum is composed as the input of neural network to characterize cell spatiotemporal deformation, then a pre-trained convolutional neural network model is performed for automatic feature extraction. Finally, the extracted deep convolutional features are analyzed by SVM. Experimental results demonstrate that the proposed strategy outperforms existing methods on the live-cell database, and the features extracted by the last layer and classified by linear kernel SVM allows for the state-of-the-art performance.