Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences
We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.
Our supplementary materials, including the video results and our features used to train the classifiers, are available here. [Download]
1. Matlab code to fit ellipses [Download]
2. Julia code to train GBT classifiers [Download]
3. C++ code to conduct detection and tracking jointly [Download]
Our initial segmentation results on 10 sequences of the cell tracking challenge [Download]
We will soon provide a more detailed readme. Other materials, such as the configuration files of our compared methods, will also be made publicly available soon.