PMID: 30171236DOI: 10.1038/s41592-018-0106-zSep 2, 2018Paper
CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation
Nature Methods
Matthias G HaberlMark H Ellisman
Abstract
As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We address these bottlenecks with CDeep3M, a ready-to-use image segmentation solution employing a cloud-based deep convolutional neural network. We benchmark CDeep3M on large and complex two-dimensional and three-dimensional imaging datasets from light, X-ray, and electron microscopy.
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Methods Mentioned
BETA
electron microscopy
X-ray
fluorescence microscopy
light microscopy
electron
transmission electron microscopy
electron tomography
scanning electron microscopy
scanning
Software Mentioned
CDeep3M
Ilastik
DeepEM3D
AWS CloudFormation
Deep3M
CloudFormation
CHM
Caffe
Xradia
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