Stem cell motion-tracking by using deep neural networks with multi-output

Original Article
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Abstract

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.

Keywords

Cell tracking Neural networks Mitosis detection Multi-output 

Notes

Acknowledgements

This work was supported by the Key Program of National Natural Science Foundation of China (61402306, 61432012, U1435213).

Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service, and company that could be construed as influencing the position presented in or the review of this manuscript.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Machine Intelligence Laboratory, College of Computer ScienceSichuan UniversityChengduPeople’s Republic of China

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