henhaobio
Sign in

bioRxivCell Biology

CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy

M. Haberl, C. Churas, L. Tindall, D. Boassa, S. Phan, E. A. Bushong, M. Madany, R. Akay, T. J. Deerinck, S. T. Peltier, M. H. Ellisman

PDFDOIPreprint

Abstract

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.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISBelevich '20Haberl '20Bhattiprolu '26Chamier '21Gomez-de-Ma '21Stringer '20
79% semantic matchCell Biology
I. Belevich, E. Jokitalo·bioRxiv·Jul 13, 2020