Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.
Juan C CaicedoAllen GoodmanKyle W KarhohsBeth A CiminiJeanelle AckermanMarzieh HaghighiCherKeng HengTim BeckerMinh DoanClaire McQuinMohammad RohbanShantanu SinghAnne E CarpenterPublished in: Nature methods (2019)
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.
Keyphrases
- deep learning
- convolutional neural network
- high resolution
- artificial intelligence
- big data
- electronic health record
- machine learning
- endothelial cells
- single molecule
- induced apoptosis
- optical coherence tomography
- data analysis
- high throughput
- cell therapy
- single cell
- cell proliferation
- fluorescence imaging
- photodynamic therapy
- stem cells
- bone marrow
- oxidative stress
- label free