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To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation\u2014including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6\n                    <jats:sup>th<\/jats:sup>\n                    edition of the Cell Tracking Challenge.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0249257","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T13:46:58Z","timestamp":1631022418000},"page":"e0249257","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":35,"title":["A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9769-3517","authenticated-orcid":true,"given":"Katharina","family":"L\u00f6ffler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8755-2825","authenticated-orcid":true,"given":"Tim","family":"Scherr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9100-5496","authenticated-orcid":true,"given":"Ralf","family":"Mikut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"pone.0249257.ref001","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/B978-0-12-394447-4.20070-9","volume-title":"Encyclopedia of Cell Biology","author":"A Ortega-Carrion","year":"2016"},{"issue":"13","key":"pone.0249257.ref002","article-title":"Defective endothelial cell migration in the absence of Cdc42 leads to capillary-venous malformations","volume":"145","author":"B Lavi\u00f1a","year":"2018","journal-title":"Development"},{"issue":"4","key":"pone.0249257.ref003","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1002\/bit.26523","article-title":"Engineered cell migration to lesions linked to autoimmune disease","volume":"115","author":"AA Mosabbir","year":"2018","journal-title":"Biotechnology and Bioengineering"},{"issue":"4","key":"pone.0249257.ref004","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/S1470-2045(04)01431-7","article-title":"Tumour-cell migration, invasion, and metastasis: navigation by neurotransmitters","volume":"5","author":"F Entschladen","year":"2004","journal-title":"The Lancet Oncology"},{"issue":"11","key":"pone.0249257.ref005","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1093\/bioinformatics\/btu080","article-title":"A benchmark for comparison of cell tracking algorithms","volume":"30","author":"M Ma\u0161ka","year":"2014","journal-title":"Bioinformatics"},{"issue":"8","key":"pone.0249257.ref006","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.semcdb.2009.07.004","article-title":"Tracking in cell and developmental biology","volume":"20","author":"E Meijering","year":"2009","journal-title":"Seminars in Cell & Developmental Biology"},{"issue":"1","key":"pone.0249257.ref007","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: a generalist algorithm for cellular segmentation","volume":"18","author":"C Stringer","year":"2021","journal-title":"Nature Methods"},{"issue":"1","key":"pone.0249257.ref008","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: deep learning for cell counting, detection, and morphometry","volume":"16","author":"T Falk","year":"2019","journal-title":"Nature Methods"},{"issue":"12","key":"pone.0249257.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0243219","article-title":"Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy","volume":"15","author":"T Scherr","year":"2020","journal-title":"PLOS ONE"},{"key":"pone.0249257.ref010","doi-asserted-by":"crossref","unstructured":"Weigert M, Schmidt U, Haase R, Sugawara K, Myers G. 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