arrow
Return

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

delete2018-08-31
delete134
delete
OA
AI
M
Matthias G. Haberl
C
Christopher Churas
L
Lucas Tindall
D
Daniela Boassa
S
Sébastien Phan
E
Eric A. Bushong
M
Matthew Madany
R
Raffi Akay
T
Thomas J. Deerinck
S
Steven T. Peltier
M
Mark H. Ellisman *
DOI:10.1038/s41592-018-0106-zdelete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
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.
Keywords:
STEREOLOGICAL ESTIMATION
NEURONS
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Nature Methods cover
Nature Methods
IF:
32.1
Papers:
7.2K
Citations:
12.7W

Organization

University of California System cover
University of California System
Scholars:
37.5W
Papers: 33.7W
Citations: 6.6K
U
University of California San Diego
Scholars:
4.6W
Papers: 3.5W
Citations: 924