SBEM, TEM Membranes (50789)
Trained model
• SBEM and TEM Membranes (Aug-110)
Description
• Electron microscopy membrane model trained 60k iterations with CDeep3M2 /// Secondary Augmentations: -1 /// Tertiary Augmentations: 10 /// Data: Transmission electron microscopy and serial block-face scanning electron microscopy data /// Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z /// and /// Haberl et al., CDeep3M-Preview: Online segmentation using the deep neural network model zoo. DOI: 10.1101/2020.03.26.010660
X Voxelsize
• 5 nm
Y Voxelsize
• 5 nm
Z Voxelsize
• 50 nm
Cellular component
• Membranes
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50789


SBEM, TEM Membranes (Aug05) (50772)
Trained model
• SBEM, TEM Membranes (Aug05)
Description
• Electron microscopy membrane model trained with CDeep3M2 /// Secondary Augmentations: 0 /// Tertiary Augmentations: 5 /// Data: Transmission electron microscopy and serial block-face scanning electron microscopy data /// Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z /// and /// Haberl et al., CDeep3M-Preview: Online segmentation using the deep neural network model zoo. DOI: 10.1101/2020.03.26.010660
X Voxelsize
• 5 nm
Y Voxelsize
• 5 nm
Z Voxelsize
• 50 nm
Cellular component
• SBEM, TEM Membranes (Aug05)
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50772


SBEM, TEM Membranes (50749)
Trained model
• SBEM and TEM Membranes (Aug-10)
Description
• Electron microscopy membrane model trained with CDeep3M2 /// Secondary Augmentations: -1 /// Tertiary Augmentations: 0 /// Data: Transmission electron microscopy and serial block-face scanning electron microscopy data /// Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z /// and /// Haberl et al., CDeep3M-Preview: Online segmentation using the deep neural network model zoo. DOI: 10.1101/2020.03.26.010660
X Voxelsize
• 5 nm
Y Voxelsize
• 5 nm
Z Voxelsize
• 50 ┬Ám
Cellular component
• Membranes
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50749


SBEM Synapses (denoised) (50725)
Trained model
• SBEM Synapses (denoised)
Description
• Model trained with multiple denoised SBEM datasets, to generally label the structural features of synapses, including postsynaptic densities and synaptic vesicles docking at the presynaptic site.
X Voxelsize
• 12 nm
Y Voxelsize
• 12 nm
Z Voxelsize
• 60 nm
Cellular component
• SBEM Synapses (denoised)
Author
• Matthias Haberl
• Matthew Madany
DOI
• https://doi.org/10.7295/W9CDEEP3M50725


SBEM Membranes (50692)
Trained model
• SBEM Membranes (denoised, resize augm.)
Description
• Electron microscopy membrane model trained with CDeep3M1.6 until 50k iterations Transfer learning applied from 50k-70k iterations with CDeep3M2 Secondary Augmentations: -1 Secondary Augmentations: 5 Data: Transmission electron microscopy and serial block-face scanning electron microscopy data. Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. doi: 10.1038/s41592-018-0106-z. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z and Haberl et al., CDeep3M-Preview: Online segmentation using the deep neural network model zoo. DOI: 10.1101/2020.03.26.010660
X Voxelsize
• 5 nm
Y Voxelsize
• 5 nm
Z Voxelsize
• 70 nm
Cellular component
• Membranes
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50692


Mitochondria (50689)
Trained model
• SBEM Mitochondria (denoised)
Description
• A broadly trained model for mitochondria segmentation based on SBEM and TEM datasets /// Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z
X Voxelsize
• 4.8 nm
Y Voxelsize
• 4.8 nm
Z Voxelsize
• 70 nm
Cellular component
• Mitochondria
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50689


SBEM Membranes (50687)
Trained model
• SBEM Membranes (denoised)
Description
• SBEM Membranes training 50k-55k with denoised images Cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. Epub 2018 Aug 31. PMID: 30171236. DOI: 10.1038/s41592-018-0106-z.
X Voxelsize
• 4.8 nm
Y Voxelsize
• 4.8 nm
Z Voxelsize
• 70 nm
Cellular component
• Membranes
Author
• Matthias Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50687


SBEM (50685)
Trained model
• Synapses
Description
• Model trained to generally label the structural features of a synapse such as synaptic vesicles docking at the membrane and post-synaptic density.
X Voxelsize
• 12 nm
Y Voxelsize
• 12 nm
Z Voxelsize
• 40 nm
Cellular component
• 
Author
• Matthew Madany
DOI
• https://doi.org/10.7295/W9CDEEP3M50685


Serial section electron tomography Membranes (50683)
Trained model
• Tomography Membranes
Description
• Trained with CDeep3M 1.6.3 (used in doi: 10.1038/s41592-018-0106-z) Sample: High-Pressure Frozen, freeze substituted tissue Data: Serial section multi-tilt electron tomography Target: Membranes
X Voxelsize
• 1.6 nm
Y Voxelsize
• 1.6 nm
Z Voxelsize
• 1.6 nm
Cellular component
• Membranes
Author
• Matthias G Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50683


SBEM Synaptic Vesicles (50682)
Trained model
• SBEM synaptic vesicles
Description
• A trained model for segmentation of synaptic vesicles based on SBEM datasets Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. doi: 10.1038/s41592-018-0106-z. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z
X Voxelsize
• 2.4 nm
Y Voxelsize
• 2.4 nm
Z Voxelsize
• 24 nm
Cellular component
• Synaptic Vesicles
Author
• Matthias G Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50682


SBEM mitochondria (50681)
Trained model
• SBEM and ssTEM Mitochondria
Description
• A broadly trained model for mitochondria segmentation Trained with CDeep3M 1.6.3 (used in doi: 10.1038/s41592-018-0106-z) Data: SBEM and TEM Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. doi: 10.1038/s41592-018-0106-z. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z
X Voxelsize
• 4.8 nm
Y Voxelsize
• 4.8 nm
Z Voxelsize
• 70 nm
Cellular component
• mitochondria
Author
• Matt Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50681


TEM (50673)
Trained model
• SBEM and TEM membranes
Description
• Transmission electron microscopy, serial block-face scanning electron microscopy Trained with CDeep3M 1.6.3 (used in doi: 10.1038/s41592-018-0106-z) Please cite: Haberl et al., CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods. 2018 Sep;15(9):677-680. doi: 10.1038/s41592-018-0106-z. Epub 2018 Aug 31. PMID: 30171236 DOI: 10.1038/s41592-018-0106-z
X Voxelsize
• 5 nm
Y Voxelsize
• 5 nm
Z Voxelsize
• 
Cellular component
• 
Author
• Matt Haberl
DOI
• https://doi.org/10.7295/W9CDEEP3M50673