How To Make Your Cell Tracker Say "I dunno!"
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913732649549824 |
|---|---|
| author | Paul, Richard D. Seiffarth, Johannes Rügamer, David Scharr, Hanno Nöh, Katharina |
| author_facet | Paul, Richard D. Seiffarth, Johannes Rügamer, David Scharr, Hanno Nöh, Katharina |
| contents | Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How To Make Your Cell Tracker Say "I dunno!" Paul, Richard D. Seiffarth, Johannes Rügamer, David Scharr, Hanno Nöh, Katharina Computer Vision and Pattern Recognition Quantitative Methods Applications Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties. |
| title | How To Make Your Cell Tracker Say "I dunno!" |
| topic | Computer Vision and Pattern Recognition Quantitative Methods Applications |
| url | https://arxiv.org/abs/2503.09244 |