ArticleScientific reports2019
Automated tracking of label-free cells with enhanced recognition of whole tracks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Modeling glioblastoma relapse in vitro: a critical journey from 2D models to organ-on-chip alternatives.NPJ precision oncology · 2026Review
- Development of label-free cell tracking for discrimination of the heterogeneous mesenchymal migration.PloS one · 2025Article
- Deep learning-based characterization of neutrophil activation phenotypes inComputational and structural biotechnology journal · 2024Article
- An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis.Chemical & biomedical imaging · 2023Article
- Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm.Cell reports methods · 2023Article
- Article
- AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma.Journal of personalized medicine · 2022Article
- Automated characterisation of neutrophil activation phenotypes inComputational and structural biotechnology journal · 2022Article
- Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach.PloS one · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.