Description Usage Arguments Value Examples
View source: R/predict_flexconn.R
Predict from FLEXCONN model
1 2 3 |
t1 |
T1-weighted image to predict from: skullstripped, bias-corrected). Since the training is 2D, make sure the test image is properly oriented, i.e. the in-plane has the highest native resolution. E.g. the training images are axial because their native resolution is 1x1x4mm^3 in axial RAI orientation. |
flair |
FLAIR image to predict from, must be registered to T1 and have same orientation as T1 |
t2 |
(optional) T2 image to predict from, must be registered to T1 and have same orientation as T1 |
outdir |
Output directory for predictions |
gpu |
Either an integer for the GPU. Use "cpu" to use CPU. |
normalize |
Should the images be normalized? |
num_atlases |
Specifies which model to use. Determined by the number of atlases in the FLEXCONN model. |
outcomes |
The outcome used to train the model, from rater 1 or rater 2 |
verbose |
Print diagnostic messages |
A vector of filenames
1 2 3 4 5 6 7 8 9 10 11 12 | # predict_flexconn(python_cmd = "python3)
library(reticulate)
## Not run:
reticulate::use_python("/Library/Frameworks/Python.framework/Versions/3.5/bin/python3")
# reticulate::use_python("python3")
flair = system.file("extdata", "FLAIR.nii.gz", package = "flexconnr")
t1 = system.file("extdata", "T1.nii.gz", package = "flexconnr")
pp = predict_flexconn(t1 = t1, flair = flair)
# result = RNifti::readNifti(pp[2])
## End(Not run)
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