  Cell segmentation and tracking in microscopy images are of great significance
to new discoveries in biology and medicine. In this study, we propose a novel
approach to combine cell segmentation and cell tracking into a unified
end-to-end deep learning based framework, where cell detection and segmentation
are performed with a current instance segmentation pipeline and cell tracking
is implemented by integrating Siamese Network with the pipeline. Besides,
tracking performance is improved by incorporating spatial information into the
network and fusing spatial and visual prediction. Our approach was evaluated on
the DeepCell benchmark dataset. Despite being simple and efficient, our method
outperforms state-of-the-art algorithms in terms of both cell segmentation and
cell tracking accuracies.
