Typical time intervals between acquisitions of three-dimensional (3-D) images of the same cell in live cell imaging are in the orders of minutes. In the meantime, the live cell can move in a water basin on the stage. This movement can hamper the studies of intranuclear processes. We propose a fast point-based image registration method for the suppression of the movement of a cell as a whole in the image data. First, centroids of certain intracellular objects are computed for each image in a time-lapse series. Then, a matching between the centroids, which have the maximal number of pairs, is sought between consecutive point sets by a 3-D extension of a two-dimensional fast point pattern matching method, which is invariant to rotation, translation, local distortion, and extra/missing points. The proposed 3-D extension assumes rotations only around the z axis to retain the complexity of the original method. The final step involves computing the optimal fully 3-D transformation between images from corresponding points in the least-squares manner. The robustness of the method was evaluated on generated data. The results of the simulations show that the method is very precise and its correctness can be estimated. This article also presents two practical application examples, namely the registration of images of HP1 domains and the registration of images of telomeres. More than 97% of time-consecutive images were successfully registered. The results show that the method is very well suited to live cell imaging.
- MeSH
- Algorithms MeSH
- Artifacts MeSH
- Financing, Organized MeSH
- Microscopy, Fluorescence methods MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Cells, Cultured cytology MeSH
- Humans MeSH
- Cell Movement MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Sensitivity and Specificity MeSH
- Subtraction Technique MeSH
- Information Storage and Retrieval methods MeSH
- Artificial Intelligence MeSH
- Microscopy, Video methods MeSH
- Image Enhancement methods MeSH
- Imaging, Three-Dimensional methods MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Evaluation Study MeSH
Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.
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
Movement of labelled plasmid DNA relative to heterochromatin foci in nuclei, visualized with HP1-GFP, was studied using live-cell imaging and object tracking. In addition to Brownian motion of plasmid DNA we found a pronounced, non-random movement of plasmid DNA towards the nearest HP1 focus, while time-lapse microscopy showed that HP1 foci are relatively immobile and positionally stable. The movement of plasmid DNA was much faster than that of the HP1 foci. Contact of transgene DNA with an HP1 focus usually resulted in cessation of the directional motion. Moreover, the motion of plasmid DNA inside the heterochromatin compartment was more restricted (limited to 0.25 microm) than when the plasmid DNA was outside heterochromatin (R = 0.7 microm). Three days after transfection most of the foreign labelled DNA colocalized with centromeric heterochromatin.
- MeSH
- Biological Transport physiology genetics MeSH
- Cell Nucleus physiology MeSH
- Chromosomal Proteins, Non-Histone physiology genetics MeSH
- DNA physiology genetics MeSH
- Financing, Organized MeSH
- Heterochromatin MeSH
- Humans MeSH
- Microscopy MeSH
- Cell Line, Tumor MeSH
- Plasmids physiology genetics MeSH
- Transfection MeSH
- Check Tag
- Humans MeSH
