ArticlePloS one2021
A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Dynamic Contour Evolution Algorithm for Cell Segmentation and Synaptic Tracking under Occlusion.Chemical & biomedical imaging · 2026Article
- EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation.bioRxiv : the preprint server for biology · 2026Article
- High-Fidelity Long-term Whole-embryo Lineage and Fate Reconstruction by Iterative Tracking with Error Correction.bioRxiv : the preprint server for biology · 2026Article
- easytrack: A napari plugin for automated parameter tuning in cell tracking.microPublication biology · 2026Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- Uncertainty-Gated Min-Cost Flows forbioRxiv : the preprint server for biology · 2025Article
- PyUAT: An open-source Python framework for uncertainty-aware, efficient, and scalable model-driven cell tracking.PloS one · 2025Article
- A genetically encoded sensor for visualizing leukotriene B4 gradients in vivo.Nature communications · 2023Article
- The Cell Tracking Challenge: 10 years of objective benchmarking.Nature methods · 2023Article
- Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects.Nature communications · 2023Article
- Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images.Scientific reports · 2022Article
- microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.PloS one · 2022Article
- Imaging-based tracking of stem-cell responses for 3D bioprinting optimization.Cell transplantationReview
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Authors and funding
3 authors.
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Abstract
Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. 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-including 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 6th edition of the Cell Tracking Challenge.
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