  Automatic cell tracking in dense environments is plagued by inaccurate
correspondences and misidentification of parent-offspring relationships. In
this paper, we introduce a novel cell tracking algorithm named DenseTrack,
which integrates deep learning with mathematical model-based strategies to
effectively establish correspondences between consecutive frames and detect
cell division events in crowded scenarios. We formulate the cell tracking
problem as a deep learning-based temporal sequence classification task followed
by solving a constrained one-to-one matching optimization problem exploiting
the classifier's confidence scores. Additionally, we present an
eigendecomposition-based cell division detection strategy that leverages
knowledge of cellular geometry. The performance of the proposed approach has
been evaluated by tracking densely packed cells in 3D time-lapse image
sequences of bacterial biofilm development. The experimental results on
simulated as well as experimental fluorescence image sequences suggest that the
proposed tracking method achieves superior performance in terms of both
qualitative and quantitative evaluation measures compared to recent
state-of-the-art cell tracking approaches.
