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CDeep3M


Adapted from: Haberl, et al., (2018) CDeep3M: plug-and-play cloud based deep learning for image segmentation, Nat Methods 15 (2018) 677–680.
Description
As biological imaging datasets increase in size, deep neural networks are considered vital tools for efficient image segmentation. While a number of different network architectures have been developed for segmenting even the most challenging biological images, community access is still limited by the difficulty of setting up complex computational environments and processing pipelines, and the availability of compute resources. Here, we address these bottlenecks, providing a ready-to-use image segmentation solution for any lab, with a pre-configured, publicly available, cloud-based deep convolutional neural network on Amazon Web Services (AWS). We provide simple instructions for training and applying CDeep3M for segmentation of large and complex 2D and 3D microscopy datasets of diverse biomedical imaging modalities. Read more.


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
Microscopy type
SBEM
Cellular component
Synaptic Vesicles
Author
Matthias G Haberl
DOI
https://doi.org/10.7295/W9CDEEP3M50682

SEMTEM membranes (50673)
Trained model
SEMTEM membranes
Description
Transmission electron microscopy, serial block-face scanning electron microscopy 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
Microscopy type
SBEM
TEM
Cellular component
membrane
Author
Matt Haberl
DOI
https://doi.org/10.7295/W9CDEEP3M50673

SBEM and ssTEM Mitochondria (50681)
Trained model
SBEM and ssTEM Mitochondria
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. 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
Microscopy type
SBEM
Cellular component
mitochondria
Author
Matt Haberl
DOI
https://doi.org/10.7295/W9CDEEP3M50681

Tomography Membranes (50683)
Trained model
Tomography Membranes
Description
High-Pressure Frozen, freeze substituted tissue Serial section multi-tilt electron tomography
X Voxelsize
1.6 nm
Y Voxelsize
1.6 nm
Z Voxelsize
1.6 nm
Microscopy type
Serial section electron tomography
Cellular component
Membranes
Author
Matthias G Haberl
DOI
https://doi.org/10.7295/W9CDEEP3M50683

Synapses (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
Microscopy type
SBEM
Author
Matthew Madany
DOI
https://doi.org/10.7295/W9CDEEP3M50685

SEMTEM membranes
Trained model
Membranes
Modality
Transmission electron microscopy, serial block-face scanning electron microscopy
Voxelsize
~5nm




Tomo Vesicles
Trained model
Synaptic Vesicles
Sample
High-Pressure Frozen, Hippocampus Mouse Brain
Modality
Serial section electron tomography
Voxelsize
1.6nm




XRM nuclei
Trained model
Nucleus segmentation
Sample
Hippocampus Mouse Brain Stained for SBEM
Modality
X-Ray micro CT
Objective
40x
Voxelsize
0.4159µm