bioimage analysis
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Similar to the medical imaging community, the bioimaging community has recently realized the need to benchmark various image analysis methods to compare their performance and assess their suitability for specific applications. Challenges sponsored by prestigious conferences have proven to be an effective means of encouraging benchmarking and new algorithm development for a particular type of image data. Bioimage analysis challenges have recently complemented medical image analysis challenges, especially in the case of the International Symposium on Biomedical Imaging (ISBI). This review summarizes recent progress in this respect and describes the general process of designing a bioimage analysis benchmark or challenge, including the proper selection of datasets and evaluation metrics. It also presents examples of specific target applications and biological research tasks that have benefited from these challenges with respect to the performance of automatic image analysis methods that are crucial for the given task. Finally, available benchmarks and challenges in terms of common features, possible classification and implications drawn from the results are analysed.
- MeSH
- algoritmy MeSH
- benchmarking * MeSH
- databáze faktografické MeSH
- fluorescenční mikroskopie přístrojové vybavení metody normy MeSH
- lidé MeSH
- molekulární zobrazování přístrojové vybavení metody normy MeSH
- počítačové zpracování obrazu metody statistika a číselné údaje MeSH
- rozpoznávání automatizované statistika a číselné údaje MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.
- MeSH
- algoritmy * MeSH
- neuronové sítě * MeSH
- Publikační typ
- časopisecké články MeSH
(1) Background: The detection of DNA double-strand breaks in vitro using the phosphorylated histone biomarker (γH2AX) is an increasingly popular method of measuring in vitro genotoxicity, as it is sensitive, specific and suitable for high-throughput analysis. The γH2AX response is either detected by flow cytometry or microscopy, the latter being more accessible. However, authors sparsely publish details, data, and workflows from overall fluorescence intensity quantification, which hinders the reproducibility. (2) Methods: We used valinomycin as a model genotoxin, two cell lines (HeLa and CHO-K1) and a commercial kit for γH2AX immunofluorescence detection. Bioimage analysis was performed using the open-source software ImageJ. Mean fluorescent values were measured using segmented nuclei from the DAPI channel and the results were expressed as the area-scaled relative fold change in γH2AX fluorescence over the control. Cytotoxicity is expressed as the relative area of the nuclei. We present the workflows, data, and scripts on GitHub. (3) Results: The outputs obtained by an introduced method are in accordance with expected results, i.e., valinomycin was genotoxic and cytotoxic to both cell lines used after 24 h of incubation. (4) Conclusions: The overall fluorescence intensity of γH2AX obtained from bioimage analysis appears to be a promising alternative to flow cytometry. Workflow, data, and script sharing are crucial for further improvement of the bioimage analysis methods.
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
Fluorescent nanodiamonds (NDs) attracted attention as a new promising type of nanoparticles for bioapplications. This carbon nanomaterial with low-toxicity is able to accommodate fluorescent nitrogen-vacancy (N-V) color centers, prime examples of non-photobleachable defects of diamond crystal lattice. An overview of ND applications in bioimaging is presented. NDs are compared with other fluorescent probes and their specific chemical and colloidal properties were shown. The biologically relevant properties of fluorescent NDs such as toxicity, biocompatibility and their cellular localization and internalization are discussed.
- MeSH
- cílená molekulární terapie využití MeSH
- fluorescence * MeSH
- fluorescenční barviva chemie MeSH
- lékové transportní systémy využití MeSH
- lidé MeSH
- luminiscence MeSH
- molekulární sondy - techniky MeSH
- molekulární zobrazování * MeSH
- nanodiamanty * chemie MeSH
- nanostruktury MeSH
- Ramanova spektroskopie MeSH
- receptory umělé * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH