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|Interface||Graphical user interface|
|Restrictions to use||Academic or non-commercial use|
|High performance computing||Yes|
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- person_outline Susumu Mori
Publication for MRICloud
Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?
[…] 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 […]
Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing
[…] 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 […]
Imaging network level language recovery after left PCA stroke
[…] 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 (; […]
Evaluation of Cross Protocol Stability of a Fully Automated Brain Multi Atlas Parcellation Tool
[…] 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 […]
Segmentation of brain magnetic resonance images based on multi atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles
[…] 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 […]
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