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CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation.

Matthias Georg HaberlChristopher ChurasLucas TindallDaniela BoassaSébastien PhanEric A BushongMatthew MadanyRaffi AkayThomas J DeerinckSteven T PeltierMark H Ellisman
Published in: Nature methods (2018)
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.
Keyphrases
  • deep learning
  • convolutional neural network
  • electron microscopy
  • high resolution
  • neural network
  • artificial intelligence
  • machine learning
  • mental health
  • healthcare
  • rna seq
  • magnetic resonance
  • single cell