  Cell event detection in cell videos is essential for monitoring of cellular
behavior over extended time periods. Deep learning methods have shown great
success in the detection of cell events for their ability to capture more
discriminative features of cellular processes compared to traditional methods.
In particular, convolutional long short-term memory (LSTM) models, which
exploits the changes in cell events observable in video sequences, is the
state-of-the-art for mitosis detection in cell videos. However, their
limitations are the determination of the input sequence length, which is often
performed empirically, and the need for a large annotated training dataset
which is expensive to prepare. We propose a novel semi-supervised method of
optimal length detection for mitosis detection with two key contributions: (i)
an unsupervised step for learning the spatial and temporal locations of cells
in their normal stage and approximating the distribution of temporal lengths of
cell events and, (ii) a step of inferring, from that distribution, an optimal
input sequence length and a minimal number of annotated frames for training a
LSTM model for each particular video. We evaluated our method in detecting
mitosis in densely packed stem cells in a phase-contrast microscopy videos. Our
experimental data prove that increasing the input sequence length of LSTM can
lead to a decrease in performance. Our results also show that by approximating
the optimal input sequence length of the tested video, a model trained with
only 18 annotated frames achieved F1-scores of 0.880-0.907, which are 10%
higher than those of other published methods with a full set of 110 training
annotated frames.
