Globally Optimal Cell Tracking using Integer Programming
This addresses the problem of accurate cell tracking for researchers in biology and medicine, offering a robust solution for clumped cells, but it appears incremental as it builds on existing tracking methods with specific improvements.
The paper tackles the problem of tracking cell populations in time-lapse images by proposing a novel approach that uses integer programming to handle occlusions and overlaps, demonstrating effectiveness on challenging sequences and outperforming state-of-the-art techniques.
We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses 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 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.