Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Consequently, a large number of manual and semi-automated time-lapse microscopy image analyses of mitosis have been carried out in recent years. In this paper, we propose a method for automatic detection of cell-cycle stages using a recurrent neural network (RNN). An end-to-end model with center-cell focus tracker loss, and classification loss is trained. The evaluation was conducted on two time-series datasets, with 6-stages and 3-stages of cell splitting labeled. The frame-to-frame accuracy was calculated and precision, recall, and F1 Score were measured for each cell-cycle stage. We also visualized the learned feature space. Image reconstruction from the center-cell focus module was performed which shows that the network was able to focus on the center cell and classify it simultaneously. Our experiments validate the superior performance of the proposed network compared to a classifier baseline.

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well, to quantify the amount of misclassification. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

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