Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images

Siteng Chen, Ao Li, Janet Roveda

Abstract: Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods.