Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
| Autor: | Shantanu Singh, Claire McQuin, Juan C. Caicedo, Marzieh Haghighi, CherKeng Heng, Jeanelle Ackerman, Anne E. Carpenter, Allen Goodman, Kyle W. Karhohs, Mohammad Hossein Rohban, Minh Doan, Beth A. Cimini, Tim Becker |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Extramural Computer science Image processing Cell Biology Biochemistry Imaging data Data science 03 medical and health sciences 0302 clinical medicine Market segmentation Human interaction Machine learning Analysis software Segmentation Analysis tools Molecular Biology 030217 neurology & neurosurgery Analysis 030304 developmental biology 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. The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation. |
| Databáze: | OpenAIRE |
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