MRICloud statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

MRICloud specifications

Information


Unique identifier OMICS_30939
Name MRICloud
Software type Application/Script
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux
Computer skills Medium
Stability Stable
High performance computing Yes
Registration required Yes
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Susumu Mori

Publication for MRICloud

MRICloud citations

 (5)
library_books

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

2017
Front Neuroinform
PMCID: 5675877
PMID: 29163119
DOI: 10.3389/fninf.2017.00063

[…] able Pipeline for the Analysis of Connectomes) is an environment to automate preprocessing and analysis of resting-state fMRI data, and is available as a machine image on EC2 (). Newer platforms like MRICloud shift neuroimaging processing entirely to the cloud, and links different types of service tools to offer an integrated software-as-a-service model that enables users to run analyses and quali […]

library_books

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

2016
PMCID: 4896536
PMID: 27279746
DOI: 10.4137/MRI.S23558

[…] mage processing pipelines, brain atlases and/or data sets, and also integrates the underlying platforms and infrastructure—for cloud-based neuroimaging analyses. Two recent examples of BiAaaS include MRICloud and NITRC-CE, which will be discussed in greater detail below.Infrastructure as a Service (IaaS) is the most basic service model (ie, the lowest level of cloud computing; ), where the cloud s […]

library_books

Imaging network level language recovery after left PCA stroke

2016
PMCID: 5003759
PMID: 27176918
DOI: 10.3233/RNN-150621

[…] f language tasks (e.g., ; ; ; ). The anatomical mask for each ROI was generated from multiple atlases (45 adult brain atlases, segmented on 289 parcels) and is available at BrainGPS (https://braingps.mricloud.org). Each participant’s high resolution MPRAGE was segmentted by using the multi-atlas mapping and parcellation approach based on the large deformation diffeomorphic metric mapping, LDDMM (; […]

library_books

Evaluation of Cross Protocol Stability of a Fully Automated Brain Multi Atlas Parcellation Tool

2015
PLoS One
PMCID: 4514626
PMID: 26208327
DOI: 10.1371/journal.pone.0133533

[…] nt manufacturers and two different field strengths. We also used ADNI AD data for pathology effect analysis. All analyses were performed based on our fully automated T1-image analysis pipeline in the MriCloud platform (www.mricloud.org). Based on this analysis, we measured the impact of protocol differences on the parcellation results, and compared its extent with two types of biological effect si […]

library_books

Segmentation of brain magnetic resonance images based on multi atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

2015
Front Neurosci
PMCID: 4347448
PMID: 25784852
DOI: 10.3389/fnins.2015.00061

[…] ifying skull-stripping and structure extraction from T1-weighted images of the human brain in a Bayesian parameter estimation setting. This fully automated hierarchical pipeline is implemented in the MriCloud platform (www.mricloud.org). The pipeline is built on a segmentation label estimation algorithm called multi-atlas likelihood fusion (MALF) (Tang et al., ). MALF relies on the information of […]

Citations

Looking to check out a full list of citations?

MRICloud institution(s)
Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins University, Whiting School of Engineering, Baltimore, MD, USA; AnatomyWorks, Baltimore, MD, USA
MRICloud funding source(s)
Supported by grants P41EB015909, R01EB017638, and R01NS084957.

MRICloud reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review MRICloud