Cell tracking using deep neural networks with multi-task learning

He, Tao, Mao, Hua, Guo, Jixiang and Yi, Zhang (2017) Cell tracking using deep neural networks with multi-task learning. Image and Vision Computing, 60. pp. 142-153. ISSN 0262-8856

He et al - Cell tracking using deep neural networks with multi-task learning AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1016/j.imavis.2016.11.010


Cell tracking plays crucial role in biomedical and computer vision areas. As cells generally have frequent deformation activities and small sizes in microscope image, tracking the non-rigid and non-significant cells is quite difficult in practice. Traditional visual tracking methods have good performances on tracking rigid and significant visual objects, however, they are not suitable for cell tracking problem. In this paper, a novel cell tracking method is proposed by using Convolutional Neural Networks (CNNs) as well as multi-task learning (MTL) techniques. The CNNs learn robust cell features and MTL improves the generalization performance of the tracking. The proposed cell tracking method consists of a particle filter motion model, a multi-task learning observation model, and an optimized model update strategy. In the training procedure, the cell tracking is divided into an online tracking task and an accompanying classification task using the MTL technique. The observation model is trained by building a CNN to learn robust cell features. The tracking procedure is started by assigning the cell position in the first frame of a microscope image sequence. Then, the particle filter model is applied to produce a set of candidate bounding boxes in the subsequent frames. The trained observation model provides the confidence probabilities corresponding to all of the candidates and selects the candidate with the highest probability as the final prediction. Finally, an optimized model update strategy is proposed to enable the multi-task observation model for the variation of the tracked cell over the entire tracking procedure. The performance and robustness of the proposed method are analyzed by comparing with other commonly-used methods. Experimental results demonstrate that the proposed method has good performance to the cell tracking problem.

Item Type: Article
Uncontrolled Keywords: Cell tracking, Deep learning, Convolutional Neural Networks, Multi-task learning
Subjects: C100 Biology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2019 11:43
Last Modified: 01 Aug 2021 11:30
URI: http://nrl.northumbria.ac.uk/id/eprint/39671

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