Alternate header for print version

Attribution Non-Commercial Share Alike:This image is licensed under a Creative Commons Attribution, Non-Commercial Share Alike License. View License Deed | View Legal Code
*CIL – Cell Image Library accession number. Please use this to reference an image.

CIL:50639*  Cite 

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.

Technical Details

Zeiss Xradia 510 Versa (Zeiss X-Ray Microscopy) operated at 40 kV (76 µA current) with ×40 magnification . Tilt series of 3201 projections using XMReconstructor (Xradia). Mouse brain, hippocampal section, from the center of the suprapyramidal blade of the dentate gyrus (DG) to the molecular layer

Biological Sources
NCBI Organism Classification
Mus musculus
Matthias Haberl
Christopher Churas
Lucas Tindall
Daniela Boassa
Sebastien Phan
Eric Bushong
Matthew Madany
Raffi Akay
Thomas Deerinck
Steven Peltier
Mark Ellisman
Digital Object Identifier (DOI)
Archival Resource Key (ARK)
Image Type
X-ray micrograph
Image Mode
recorded image
Spatial Axis Image Size Pixel Size
X 940px 391µm
Y 974px 405µm
Z 948px 395µm