The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation
Open accessAbstract
As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We address these bottlenecks with CDeep3M, a ready-to-use image segmentation solution employing a cloud-based deep convolutional neural network. We benchmark CDeep3M on large and complex two-dimensional and three-dimensional imaging datasets from light, X-ray, and electron microscopy. CDeep3M provides a user-friendly tool for deep-learning-based image segmentation via a cloud-based deep convolutional neural network. Demonstrations include challenging light, X-ray, and electron microscopy segmentation tasks.
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Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Wiring specificity in the direction-selectivity circuit of the retina
Briggman, K. L., Helmstaedter, M., & Denk, W. (2011) · Nature · doi:10.1038/nature09818 · Cited by 883
Automated synaptic connectivity inference for volume electron microscopy
Dorkenwald, S., Schubert, P. J., Killinger, M. F., Urban, G., Mikula, S., Svara, F., & Kornfeld, J. (2017) · Nature Methods · doi:10.1038/nmeth.4206 · Cited by 132
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Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Greenwald, N. F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C. C., McIntosh, B. J., Leow, K. X., Schwartz, M. S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01094-0 · Cited by 953
Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206
CAVE: Connectome Annotation Versioning Engine
Dorkenwald, S., Schneider-Mizell, C. M., Brittain, D., Halageri, A., Jordan, C., Kemnitz, N., Castro, M. A., Silversmith, W., Maitin-Shephard, J., Troidl, J., et al. (2023) · doi:10.1101/2023.07.26.550598 · Cited by 79
Metadata
- Authors
- Haberl, M. G., Churas, C., Tindall, L., Boassa, D., Phan, S., Bushong, E. A., Madany, M., Akay, R., Deerinck, T. J., Peltier, S. T., & Ellisman, M. H.
- Venue
- Nature Methods
- Published
- 2018-08-31
- Volume
- 15
- Issue
- 9
- Pages
- 677-680
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2888612993
- Cited by
- 180
- References
- 5
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- green
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- Haberl, M. G., Churas, C., Tindall, L., Boassa, D., Phan, S., Bushong, E. A., Madany, M., Akay, R., Deerinck, T. J., Peltier, S. T., & Ellisman, M. H. (2018). CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nature Methods. https://doi.org/10.1038/s41592-018-0106-z
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