A human-in-the-loop approach for faster cell tracking
- 1Radboudumc
Abstract
Traditional cell tracking methods focus on fully automated tracking. The quality of the generated tracks, however, is heavily influenced by factors such as cell speed and density. In complex scenarios where track correctness is essential, an efficient and reliable tool for manual track correction remains a key component of the cell tracking process. Most tracking tools that allow for such manual correction do so only as a post-processing step, using human feedback to correct one track error at a time -- without improving the quality of other tracks in the dataset. This highlights the need for a more efficient track correction method that iteratively integrates human feedback into the automated tracking process. In this paper we propose a human-in-the-loop approach to cell tracking and investigate the extent to which a tracking algorithm can learn from human input during the tracking process. We combine a graph representation of cell detections, Dijkstra's shortest path algorithm, and a neural network that predicts cell displacement to generate initial track suggestions. We introduce a tool that enables human feedback on individual tracks suggested by the algorithm; this feedback is then propagated to improve subsequent track generation. Compared to the traditional approach of fully automated tracking with correction as a post-processing step, our human-in-the-loop tracking framework reduces the number of corrections required. Finally, we show how this approach can be integrated with existing state-of-the-art tracking algorithms by using pre-generated tracks as a prior, improving the quality of tracks suggested to the human annotator.
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BibTeX
@misc{mihaylova2026humanintheloop,
title = {{A human-in-the-loop approach for faster cell tracking}},
author = {M. Mihaylova and A. Ankan and S. Sultan and I. M. Wortel and J. Postat and M. Harris and M. Merino and J. N. Mandl and J. Textor},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.756962},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.756962v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A human-in-the-loop approach for faster cell tracking
AU - M. Mihaylova
AU - A. Ankan
AU - S. Sultan
AU - I. M. Wortel
AU - J. Postat
AU - M. Harris
AU - M. Merino
AU - J. N. Mandl
AU - J. Textor
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.756962
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.756962v1
AB - Traditional cell tracking methods focus on fully automated tracking. The quality of the generated tracks, however, is heavily influenced by factors such as cell speed and density. In complex scenarios where track correctness is essential, an efficient and reliable tool for manual track correction remains a key component of the cell tracking process. Most tracking tools that allow for such manual correction do so only as a post-processing step, using human feedback to correct one track error at a time -- without improving the quality of other tracks in the dataset. This highlights the need for a more efficient track correction method that iteratively integrates human feedback into the automated tracking process. In this paper we propose a human-in-the-loop approach to cell tracking and investigate the extent to which a tracking algorithm can learn from human input during the tracking process. We combine a graph representation of cell detections, Dijkstra's shortest path algorithm, and a neural network that predicts cell displacement to generate initial track suggestions. We introduce a tool that enables human feedback on individual tracks suggested by the algorithm; this feedback is then propagated to improve subsequent track generation. Compared to the traditional approach of fully automated tracking with correction as a post-processing step, our human-in-the-loop tracking framework reduces the number of corrections required. Finally, we show how this approach can be integrated with existing state-of-the-art tracking algorithms by using pre-generated tracks as a prior, improving the quality of tracks suggested to the human annotator.
ER -