The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
A better understanding of the molecular mechanisms leading to mast cell migration and chemotaxis is the long-term goal in mast cell research and is essential for comprehension of mast cell function in health and disease. Various techniques have been developed in recent decades for in vitro and in vivo assessment of mast cell motility and chemotaxis. In this chapter, three microscopy assays facilitating real-time quantification of mast cell chemotaxis and migration are described, focusing on individual cell tracking and data analysis.
- Keywords
- Cell migration, Cell tracking, Chemoattractant, Chemokine, Chemotaxis, Mast cells,
- MeSH
- Cell Migration Assays methods MeSH
- Biological Assay methods MeSH
- Cell Tracking methods MeSH
- Chemotaxis physiology MeSH
- Fibronectins metabolism MeSH
- Humans MeSH
- Mast Cells cytology physiology MeSH
- Microscopy methods MeSH
- Mice MeSH
- Computer Systems MeSH
- Cell Movement physiology MeSH
- Environment, Controlled MeSH
- Sepharose MeSH
- Software MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Fibronectins MeSH
- Sepharose MeSH
Magnetic resonance imaging (MRI) of superparamagnetic iron oxide-labeled cells can be used as a non-invasive technique to track stem cells after transplantation. The aim of this study was to (1) evaluate labeling efficiency of D-mannose-coated maghemite nanoparticles (D-mannose(γ-Fe2O3)) in neural stem cells (NSCs) in comparison to the uncoated nanoparticles, (2) assess nanoparticle utilization as MRI contrast agent to visualize NSCs transplanted into the mouse brain, and (3) test nanoparticle biocompatibility. D-mannose(γ-Fe2O3) labeled the NSCs better than the uncoated nanoparticles. The labeled cells were visualized by ex vivo MRI and their localization subsequently confirmed on histological sections. Although the progenitor properties and differentiation of the NSCs were not affected by labeling, subtle effects on stem cells could be detected depending on dose increase, including changes in cell proliferation, viability, and neurosphere diameter. D-mannose coating of maghemite nanoparticles improved NSC labeling and allowed for NSC tracking by ex vivo MRI in the mouse brain, but further analysis of the eventual side effects might be necessary before translation to the clinic.
- Keywords
- brain, maghemite, magnetic resonance imaging, mouse, nanoparticles, neural stem cells,
- MeSH
- Cell Tracking methods MeSH
- Magnetic Resonance Imaging methods MeSH
- Magnetite Nanoparticles chemistry MeSH
- Mannose chemistry MeSH
- Brain cytology MeSH
- Mice, Inbred C57BL MeSH
- Mice MeSH
- Neural Stem Cells cytology transplantation MeSH
- Ferric Compounds chemistry MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- ferric oxide MeSH Browser
- Magnetite Nanoparticles MeSH
- Mannose MeSH
- Ferric Compounds MeSH
We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
- MeSH
- Algorithms * MeSH
- Benchmarking MeSH
- Cell Line MeSH
- Cell Tracking methods MeSH
- Image Interpretation, Computer-Assisted * MeSH
- Humans MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Spinal cord injury (SCI) is a devastating condition that usually results in sudden and long-lasting locomotor and sensory neuron degeneration below the lesion site. During the last two decades, the search for new therapies has been revolutionized with the improved knowledge of stem cell (SC) biology. SCs therapy offers several attractive strategies for spinal cord repair. The transplantation of SCs promotes remyelination, neurite outgrowth and axonal elongation, and activates resident or transplanted progenitor cells across the lesion cavity. However, optimized growth and differentiation protocols along with reliable safety assays should be established prior to the clinical application of SCs. Additionally, the ideal method of SCs labeling for efficient cell tracking after SCI remains a challenging issue that requires further investigation. This review summarizes the current findings on the SCs-based therapeutic strategies, and compares different SCs labeling approaches for SCI.
- Keywords
- spinal cord injury, stem cell labeling, stem cells,
- MeSH
- Cell Tracking methods MeSH
- Humans MeSH
- Neural Stem Cells cytology transplantation MeSH
- Neurogenesis MeSH
- Spinal Cord Injuries diagnostic imaging pathology therapy MeSH
- Nerve Regeneration MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.
- MeSH
- Algorithms MeSH
- Cell Line MeSH
- Cell Tracking methods MeSH
- Time-Lapse Imaging methods MeSH
- Microscopy, Fluorescence MeSH
- Humans MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
Coprecipitation of FeCl2 and FeCl3 with aqueous ammonia was used to prepare iron oxide nanoparticles dispersible in aqueous medium. Oxidation of the particles with sodium hypochlorite then yielded maghemite (γ-Fe2 O3 ) nanoparticles which were coated with two types of coating -d-mannose or poly(l-lysine) (PLL) as confirmed by FTIR analysis. The particles were <10 nm according to transmission electron microscopy. Their hydrodynamic particle size was ∼180 nm (by dynamic light scattering). The d-mannose-, PLL-coated, and neat γ-Fe2 O3 particles as well as commercial Resovist® were used to label rat macrophages. The viability and contrast properties of labeled macrophages were compared. PLL-coated γ-Fe2 O3 nanoparticles were found optimal. The labeled macrophages were injected to rats monitored in vivo by magnetic resonance imaging up to 48 h. Transport of macrophages labeled with PLL-γ-Fe2 O3 nanoparticles in rats was confirmed. Tracking of macrophages using the developed particles can be used for monitoring of inflammations and cell migration in cell therapy.
- Keywords
- MRI, iron oxide, labeling, macrophages, nanoparaticles,
- MeSH
- Cell Tracking methods MeSH
- Contrast Media * chemistry pharmacology MeSH
- Rats MeSH
- Magnetic Resonance Imaging methods MeSH
- Macrophages diagnostic imaging MeSH
- Nanoparticles chemistry MeSH
- Polylysine * chemistry pharmacology MeSH
- Radiography MeSH
- Particle Size MeSH
- Ferric Compounds * chemistry pharmacology MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Male MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- ferric oxide MeSH Browser
- Contrast Media * MeSH
- Polylysine * MeSH
- Ferric Compounds * MeSH
OBJECTIVE: Cell therapies have emerged as a promising approach in medicine. The basis of each therapy is the injection of 1-100×10(6) cells with regenerative potential into some part of the body. Mesenchymal stromal cells (MSCs) are the most used cell type in the cell therapy nowadays, but no gold standard for the labeling of the MSCs for magnetic resonance imaging (MRI) is available yet. This work evaluates our newly synthesized uncoated superparamagnetic maghemite nanoparticles (surface-active maghemite nanoparticles - SAMNs) as an MRI contrast intracellular probe usable in a clinical 1.5 T MRI system. METHODS: MSCs from rat and human donors were isolated, and then incubated at different concentrations (10-200 μg/mL) of SAMN maghemite nanoparticles for 48 hours. Viability, proliferation, and nanoparticle uptake efficiency were tested (using fluorescence microscopy, xCELLigence analysis, atomic absorption spectroscopy, and advanced microscopy techniques). Migration capacity, cluster of differentiation markers, effect of nanoparticles on long-term viability, contrast properties in MRI, and cocultivation of labeled cells with myocytes were also studied. RESULTS: SAMNs do not affect MSC viability if the concentration does not exceed 100 μg ferumoxide/mL, and this concentration does not alter their cell phenotype and long-term proliferation profile. After 48 hours of incubation, MSCs labeled with SAMNs show more than double the amount of iron per cell compared to Resovist-labeled cells, which correlates well with the better contrast properties of the SAMN cell sample in T2-weighted MRI. SAMN-labeled MSCs display strong adherence and excellent elasticity in a beating myocyte culture for a minimum of 7 days. CONCLUSION: Detailed in vitro tests and phantom tests on ex vivo tissue show that the new SAMNs are efficient MRI contrast agent probes with exclusive intracellular uptake and high biological safety.
- Keywords
- magnetic resonance imaging, mesenchymal stromal cells, stem cell labeling, stem cell tracking, superparamagnetic iron oxide nanoparticles,
- MeSH
- Cell Tracking methods MeSH
- Dextrans chemistry pharmacokinetics toxicity MeSH
- Cell Physiological Phenomena drug effects MeSH
- Contrast Media chemistry pharmacokinetics toxicity MeSH
- Rats MeSH
- Cells, Cultured MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Magnetite Nanoparticles chemistry toxicity MeSH
- Mesenchymal Stem Cells chemistry cytology drug effects metabolism MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
- Names of Substances
- Dextrans MeSH
- ferumoxides MeSH Browser
- Contrast Media MeSH
- Magnetite Nanoparticles MeSH
A better understanding of the molecular mechanisms leading to mast cell migration and chemotaxis is the long-term goal in mast cell research and is essential for comprehension of mast cell function in health and disease. Various techniques have been developed in recent decades for in vitro and in vivo assessment of mast cell motility and chemotaxis. In this chapter three microscopy assays facilitating real-time quantification of mast cell chemotaxis and migration are described, focusing on individual cell tracking and data analysis.
- MeSH
- Cell Migration Assays methods MeSH
- Cell Culture Techniques instrumentation methods MeSH
- Cell Tracking methods MeSH
- Chemotactic Factors pharmacology MeSH
- Chemotaxis MeSH
- Cells, Cultured MeSH
- Humans MeSH
- Mast Cells cytology drug effects physiology MeSH
- Microscopy methods MeSH
- Cell Movement * drug effects MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Chemotactic Factors MeSH
MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.
