knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of MriCloudR is to wraps the MRICloud API so that it can be accessed from R.
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("muschellij2/MriCloudR")
Because MriCloudR
currently only works with email/password combinations, we have to store our passwords. The keyringr
package has a great vignette to show how to store your password by the password manaager for your operating system. In our example, we have named the keychain (OS X) entry as mricloudr
.
library(MriCloudR) library(keyringr) # MriCloud object. Submit requests and retrieve results. mypwd <- decrypt_kc_pw("mricloudr") mriCloudR <- MriCloudR(verbose = TRUE) # Login using MriCloud credentials. Currently, standard credentials are # supported, not OpenId login(mriCloudR, "youremail@email.com", mypwd)
If you have previous jobs in your queue, you can see your job identifiers with listJobs
listJobs(mriCloudR)
Here we download a nifti
image from the Human Connectome Project using the neurohcp
package:
library(neurohcp) img = "HCP_1200/102614/T1w/T1w_acpc_dc.nii.gz" img = download_hcp_file(img)
Now that we have the downloaded image, we have to convert it to ANALYZE format because this is required for MRICloud
library(ANTsR) img = antsImageRead(img) tfile = tempfile(fileext = ".hdr") antsImageWrite(r, filename = tfile) hdr = tfile dat = sub("[.]hdr$", ".img", hdr)
Here we create a T1SegData
object which contains payload information:
# Create T1SegData object which contains payload information t1SegData <- T1SegData() t1SegData$sliceType <- "Axial" t1SegData$hdr <- hdr t1SegData$img <- dat t1SegData$age <- 40 t1SegData$description <- "Testing" t1SegData$atlas <- "Adult_286labels_10atlases_V5L" # submit to perform t1Seg. Get back jobId. jobId <- t1Seg(mriCloudR, t1SegData)
Now we have the jobId
, isJobFinished
checks status of job. We can also see this new job ID in listJobs
:
if (isJobFinished(mriCloudR, jobId = jobId)) { print("Finished"); } else { print(paste(c("Job ", jobId, " not completed yet!"), collapse = '')) }
After the job is finished, you can download the result using downloadResult
:
# downloadResult will download the result if the jobId is finished. If the # argument waitForJobToFinish is TRUE, then downloadResult will wait until the # job is completed (checking every minute), and then download the result. x = downloadResult(mriCloudR, jobId = jobId, waitForJobToFinish = TRUE)
Now that the result was downloaded, it is a zip file, and we can unzip it using unzip
:
tdir = tempfile() dir.create(tdir, showWarnings = FALSE) unz = unzip(x, exdir = tdir)
Here we can read in the 286 labels from the segmentation:
seg_hdr = unz[grepl("_286Labels.hdr$", unz)] res = antsImageRead(seg_hdr)
Please see T1Example.r and DtiExample.r for examples on using the interfaces. They may be run via Rscript:
Rscript T1Example.r
and
Rscript DtiExample.r
0.9.0 Initial release supporting T1 segmentation
0.9.1 Added Dti segmentation and adjusted default mricloud URL
0.9.2 Changed the directory structure so that it can be submitted to Neuroconductor
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