No CrossRef data available.
Article contents
DeepSerialBlockFace: Machine denoising and object segmentation for volume electron microscopy
Published online by Cambridge University Press: 30 July 2021
Abstract
An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
- Type
- Challenges and Advances in Electron Microscopy Research and Diagnosis of Diseases in Humans, Plants and Animals (FIG associated)
- Information
- Copyright
- Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America
References
Sommer, C, Straehle, C, Köthe, U, Hamprecht, FA, editors. Ilastik: Interactive learning and segmentation toolkit. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2011 30 March-2 April 2011.CrossRefGoogle Scholar
Fang, L, Monroe, F, Novak, SW, Kirk, L, Schiavon, CR, Yu, SB, Zhang, T, Wu, M, Kastner, K, Kubota, Y, Zhang, Z, Pekkurnaz, G, Mendenhall, J, Harris, K, Howard, J, Manor, U. Deep Learning-Based Point-Scanning Super-Resolution Imaging. bioRxiv. 2019:740548. doi: 10.1101/740548.CrossRefGoogle Scholar
Falk, T, Mai, D, Bensch, R, Çiçek, Ö, Abdulkadir, A, Marrakchi, Y, Böhm, A, Deubner, J, Jäckel, Z, Seiwald, K, Dovzhenko, A, Tietz, O, Dal Bosco, C, Walsh, S, Saltukoglu, D, Tay, TL, Prinz, M, Palme, K, Simons, M, Diester, I, Brox, T, Ronneberger, O. U-Net: deep learning for cell counting, detection, and morphometry. Nature methods. 2019;16(1):67-70. Epub 2018/12/19. doi: 10.1038/s41592-018-0261-2. PubMed PMID: 30559429.CrossRefGoogle ScholarPubMed
You have
Access