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CDeep3M

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.

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



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




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