Measures for ranking cell trackers without manual validation

•We develop measures for assessing cell tracking quality without manual validation.•Information available without manual validation includes distances between cells.•We use the distances to estimate the precision and recall of the cell tracker.•We evaluate our measures under a variety of cell tracking conditions.•In practical scenarios, our performance measures correlate with traditional measures.

Cell tracking is often implemented as cell detection and data association steps. For a particular detection output it is a challenge to automatically select the best association algorithm. We approach this challenge by developing novel measures for ranking the association algorithms according to their performance without the need for a ground truth. We formulate tracking as a binary classification task and develop our principal measure (ED-score) based on the definitions of precision and recall. On a range of real cell videos tested, ED-score has a strong correlation (−0.87) with F-score. However, ED-score does not require a ground truth for computation.