Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Open accessAbstract
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.
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The ensemble of models for nuclear segmentation did not show an improvement compared to the generalist model.
“However, we did not see an improvement for the ensemble of models compared to the generalist model (Extended Data Fig. 2), consistent with the results of ref. 27.”
From Cellpose 2.0: how to train your own model (2022) · cites this paper as [27]
The Results section (‘Top solutions improve usability and accuracy of nucleus segmentation’; page unavailable) reports that five models trained separately for image types did not reach the performance of the top solution. That is consistent with the citing sentence’s general point, but it is not a direct comparison of an ensemble against a generalist model: the paper describes the top solution itself as a 32-network ensemble. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe LiveCell study used a different average precision score from ours, which additionally requires a confidence score per ROI, while we used the average precision formulation from the Data Science Bowl challenge and other studies.
“Finally, we point out that the LiveCell study used a different average precision score from ours, which additionally requires a confidence score per ROI, while we used the average precision formulation from the Data Science Bowl challenge and other studies12,27,35.”
From Cellpose 2.0: how to train your own model (2022) · cites this paper as [27]
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- Authors
- Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., Rohban, M., Singh, S., & Carpenter, A. E.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Carpenter, Anne E.
- ORCID
- Venue
- Nature Methods
- Published
- 2019-10-21
- Volume
- 16
- Issue
- 12
- Pages
- 1247-1253
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6919559
- OpenAlex
- W2980998394
- Cited by
- 809
- References
- 49
- Citations by year
- 2026: 86 · 2025: 142 · 2024: 150 · 2023: 144 · 2022: 124 · 2021: 114 · 2020: 44 · 2019: 4
- FWCI
- 34.63
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-01-22)
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Eli Lilly and Company
- Nvidia
- Broad Institute
- National Institutes of Health
- National Institute of General Medical Sciences (R35GM122547)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- nuclei segmentation, bioimage analysis, deep learning, microscopy image analysis, automated image analysis
- MeSH terms
- Data Science, Cell Nucleus, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence
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