Journal article
Measures for ranking cell trackers without manual validation
A Kan, C Leckie, J Bailey, J Markham, R Chakravorty
Pattern Recognition | ELSEVIER SCI LTD | Published : 2013
Abstract
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. © 2013 Elsevier Ltd.
Grants
Funding Acknowledgements
We would like to thank Dr. Khuloud Jaqaman (Harvard Medical School) for the advice regarding u-track; Dr. Zhaozheng Yin (Carnegie Mellon University) for sharing the videos of wound healing assays; and Dr. Daniel Day (Swinburne University of Technology) for supplying the 125 mu m microgrids. This work is partially supported by National ICT Australia (NICTA). NICTA is funded by the Australian Government's Backing Australia's Ability initiative, in part through the Australian Research Council.