# Globally Optimal Cell Tracking using Integer Programming Source: [Globally Optimal Cell Tracking using Integer Programming]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:15:30.656Z Authors: Engin T\\"uretken, Xinchao Wang, Carlos Becker, Carsten Haubold, Pascal Fua Published: 2015-01-22T13:51:06.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cell Tracking on CTC HeLa-01 (train)](): Cell tracking; CTC HeLa-01 (train); TRA: 98. - [Cell Tracking on CTC HeLa-02 (train)](): Cell tracking; CTC HeLa-02 (train); TRA: 97. - [Cell Tracking on GOWT1-02 CTC (train)](): Cell tracking; GOWT1-02 CTC (train); TRA (Tracking Accuracy): 95. ## Original sources and implementation [Original paper]() [PDF]()