The aim of automated stem cell motility analysis is reliable processing and evaluation of cell behaviors such as translocation, mitosis, death, and so on. Cell tracking plays an important role in this research. In practice, tracking stem cells is difficult because they have frequent motion, deformation activities, and small resolution sizes in microscopy images. Previous tracking approaches designed to address this problem have been unable to generalize the rapid morphological deformation of cells in a complex living environment, especially for real-time tracking tasks. Herein, a deep learning framework with convolutional structure and multi-output layers is proposed for overcoming stem cell tracking problems. A convolutional structure is used to learn robust cell features through deep features learned on massive visual data by a transfer learning strategy. With multi-output layers, this framework tracks the cell's motion and simultaneously detects its mitosis as an assistant task. This improves the generalization ability of the model and facilitates practical applications for stem cell research. The proposed framework, tracking and detection neural networks, also contains a particle filter-based motion model, a specialized cell sampling strategy, and corresponding model update strategy. Its current application to a microscopy image dataset of human stem cells demonstrates increased tracking performance and robustness compared with other frequently used methods. Moreover, mitosis detection performance was verified against manually labeled mitotic events of the tracked cell. Experimental results demonstrate good performance of the proposed framework for addressing problems associated with stem cell tracking.
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