001049900 001__ 1049900
001049900 005__ 20260107202238.0
001049900 037__ $$aFZJ-2025-05660
001049900 1001_ $$0P:(DE-Juel1)175101$$aPaul, Richard Dominik$$b0$$eCorresponding author$$ufzj
001049900 1112_ $$aInternational Conference on Computer Vision, ICCV 2025$$cHonolulu$$d2025-10-19 - 2025-10-23$$wUSA
001049900 245__ $$aHow To Make Your Cell Tracker Say "I dunno!"
001049900 260__ $$c2025
001049900 29510 $$aProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
001049900 300__ $$a6914-6923
001049900 3367_ $$2ORCID$$aCONFERENCE_PAPER
001049900 3367_ $$033$$2EndNote$$aConference Paper
001049900 3367_ $$2BibTeX$$aINPROCEEDINGS
001049900 3367_ $$2DRIVER$$aconferenceObject
001049900 3367_ $$2DataCite$$aOutput Types/Conference Paper
001049900 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1767813071_15420
001049900 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
001049900 520__ $$aCell 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.
001049900 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001049900 7001_ $$0P:(DE-Juel1)176923$$aSeiffarth, Johannes$$b1$$ufzj
001049900 7001_ $$0P:(DE-HGF)0$$aRügamer, David$$b2
001049900 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b3$$ufzj
001049900 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b4$$ufzj
001049900 8564_ $$uhttps://openaccess.thecvf.com/content/ICCV2025/html/Paul_How_To_Make_Your_Cell_Tracker_Say_I_dunno_ICCV_2025_paper.html
001049900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)175101$$aForschungszentrum Jülich$$b0$$kFZJ
001049900 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)175101$$aLMU$$b0
001049900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176923$$aForschungszentrum Jülich$$b1$$kFZJ
001049900 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a LMU$$b2
001049900 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a MCML$$b2
001049900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129394$$aForschungszentrum Jülich$$b3$$kFZJ
001049900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129051$$aForschungszentrum Jülich$$b4$$kFZJ
001049900 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001049900 920__ $$lyes
001049900 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
001049900 9201_ $$0I:(DE-Juel1)IBG-1-20101118$$kIBG-1$$lBiotechnologie$$x1
001049900 980__ $$acontrib
001049900 980__ $$aVDB
001049900 980__ $$acontb
001049900 980__ $$aI:(DE-Juel1)IAS-8-20210421
001049900 980__ $$aI:(DE-Juel1)IBG-1-20101118
001049900 980__ $$aUNRESTRICTED