Nejvíce citovaný článek - PubMed ID 19291805
Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry
Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community.
- Klíčová slova
- benchmarking, bioimaging, community, deep learning, deployment, image analysis, reproducibility, software, web application,
- Publikační typ
- časopisecké články MeSH
MOTIVATION: Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use. RESULTS: We present a modern, modular web-interface, CytoPacq, to facilitate the generation of synthetic benchmark datasets relevant for multi-dimensional cell imaging. CytoPacq poses a user-friendly graphical interface with contextual tooltips and currently allows a comfortable access to various cell simulation systems of fluorescence microscopy, which have already been recognized and used by the scientific community, in a straightforward and self-contained form. AVAILABILITY AND IMPLEMENTATION: CytoPacq is a publicly available online service running at https://cbia.fi.muni.cz/simulator. More information about it as well as examples of generated bioimage datasets are available directly through the web-interface. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
- MeSH
- počítačová simulace MeSH
- software * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The eukaryotic nucleus is a highly complex structure that carries out multiple functions primarily needed for gene expression, and among them, transcription seems to be the most fundamental. Diverse approaches have demonstrated that transcription takes place at discrete sites known as transcription factories, wherein RNA polymerase II (RNAP II) is attached to the factory and immobilized while transcribing DNA. It has been proposed that transcription factories promote chromatin loop formation, creating long-range interactions in which relatively distant genes can be transcribed simultaneously. In this study, we examined long-range interactions between the POU5F1 gene and genes previously identified as being POU5F1 enhancer-interacting, namely, CDYL, TLE2, RARG, and MSX1 (all involved in transcriptional regulation), in human pluripotent stem cells (hPSCs) and their early differentiated counterparts. As a control gene, RUNX1 was used, which is expressed during hematopoietic differentiation and not associated with pluripotency. To reveal how these long-range interactions between POU5F1 and the selected genes change with the onset of differentiation and upon RNAP II inhibition, we performed three-dimensional fluorescence in situ hybridization (3D-FISH) followed by computational simulation analysis. Our analysis showed that the numbers of long-range interactions between specific genes decrease during differentiation, suggesting that the transcription of monitored genes is associated with pluripotency. In addition, we showed that upon inhibition of RNAP II, long-range associations do not disintegrate and remain constant. We also analyzed the distance distributions of these genes in the context of their positions in the nucleus and revealed that they tend to have similar patterns resembling normal distribution. Furthermore, we compared data created in vitro and in silico to assess the biological relevance of our results.
- Publikační typ
- časopisecké články 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.
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.
