The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
Open accessAbstract
DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics. DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy.
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Cites
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0612-7 · Cited by 809
Cited by
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393
“Here, CNNs play a critical role in multiplex imaging, 143 where multiple targets within a single sample are labeled and visualized simultaneously.”
Self-citationThe ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis
Schroeder, A. B., Dobson, E. T. A., Rueden, C. T., Tomancak, P., Jug, F., & Eliceiri, K. W. (2021) · Protein Science · doi:10.1002/pro.3993 · Cited by 391
Shah, A., Schiller, J. A., Ramos, I., Serrano, J., Adams, D. K., Tawfick, S., & Ertekin, E. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.106127 · Cited by 23
Metadata
- Authors
- Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A., & Sage, D.
- Venue
- Nature Methods
- Published
- 2021-09-30
- Volume
- 18
- Issue
- 10
- Pages
- 1192-1195
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3203864625
- Cited by
- 218
- References
- 3
- FWCI
- 16.82
- Type
- journal article
- Language
- en
- Open access
- green
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- Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A., & Sage, D. (2021). DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods. https://doi.org/10.1038/s41592-021-01262-9
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