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

142Citations
Citations of this article
321Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Haberl, M. G., Churas, C., Tindall, L., Boassa, D., Phan, S., Bushong, E. A., … Ellisman, M. H. (2018). CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nature Methods, 15(9), 677–680. https://doi.org/10.1038/s41592-018-0106-z

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free