Publisher Correction: Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
| Autor: | Juan C. Caicedo, Kyle W. Karhohs, Shantanu Singh, Anne E. Carpenter, CherKeng Heng, Claire McQuin, Jeanelle Ackerman, Tim Becker, Minh Doan, Beth A. Cimini, Marzieh Haghighi, Allen Goodman, Mohammad Hossein Rohban |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Cell Nucleus
Information retrieval business.industry Computer science Published Erratum Data Science MEDLINE Cell Biology Biochemistry Publisher Correction Text mining medicine.anatomical_structure Image processing Microscopy Fluorescence Machine learning medicine Image Processing Computer-Assisted Humans Segmentation business Molecular Biology Nucleus Biotechnology |
| Zdroj: | Nature Methods |
| ISSN: | 1548-7105 1548-7091 |
| Popis: | Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools. |
| Databáze: | OpenAIRE |
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