SuBLIME_prediction: Gets predicted probabilities from SuBLIME

In neuroconductor-devel/sublime: Automatic Lesion Incidence Estimation and Detection using Multi-Modality Longitudinal Magnetic Resonance Images

Gets predicted probabilities from SuBLIME

Description

Takes in MRI images from followup and gets predictions (probabilities) of the enhancing of lesions

Usage

SuBLIME_prediction(
  baseline_flair,
  follow_up_flair,
  baseline_pd = NULL,
  follow_up_pd = NULL,
  baseline_t2,
  follow_up_t2,
  baseline_t1,
  follow_up_t1,
  time_diff,
  baseline_nawm_mask = NULL,
  follow_up_nawm_mask = baseline_nawm_mask,
  brain_mask,
  model = sublime::sublime_model,
  voxsel = TRUE,
  smooth.using = c("GaussSmoothArray", "none"),
  voxsel.sigma = diag(3, 3),
  voxsel.ksize = 5,
  s.sigma = diag(3, 3),
  s.ksize = 3,
  plot.imgs = FALSE,
  slice = 90,
  pdfname = "diag.pdf",
  verbose = TRUE
)

sublime_prediction(
  baseline_flair,
  follow_up_flair,
  baseline_pd = NULL,
  follow_up_pd = NULL,
  baseline_t2,
  follow_up_t2,
  baseline_t1,
  follow_up_t1,
  time_diff,
  baseline_nawm_mask = NULL,
  follow_up_nawm_mask = baseline_nawm_mask,
  brain_mask,
  model = sublime::sublime_model,
  voxsel = TRUE,
  smooth.using = c("GaussSmoothArray", "none"),
  voxsel.sigma = diag(3, 3),
  voxsel.ksize = 5,
  s.sigma = diag(3, 3),
  s.ksize = 3,
  plot.imgs = FALSE,
  slice = 90,
  pdfname = "diag.pdf",
  verbose = TRUE
)

Arguments

baseline_flair	Baseline FLAIR image, either array or class nifti
follow_up_flair	Followup FLAIR image, either array or class nifti
baseline_pd	Baseline PD image, either array or class nifti
follow_up_pd	Followup PD image, either array or class nifti
baseline_t2	Baseline T2 image, either array or class nifti
follow_up_t2	Followup T2 image, either array or class nifti
baseline_t1	Baseline T1 image, either array or class nifti
follow_up_t1	Followup T1 image, either array or class nifti
time_diff	Difference in time (in days) between baseline and followup, numeric
baseline_nawm_mask	Baseline Normal Appearing white matter mask, either array or class nifti. Will be coerced to logical usign baseline_nawm_mask $> 0$. If NULL, no NAWM normalization is done (assumes data is already normalized)
follow_up_nawm_mask	Followup Normal Appearing white matter mask, either array or class nifti. Will be coerced to logical usign follow_up_nawm_mask $> 0$. Defaults to baseline_nawm_mask if not specified. If NULL, no NAWM normalization is done (assumes data is already normalized)
brain_mask	Brain mask, either array or class nifti. Will be #' coerced to logical usign brain_mask $> 0$.
model	Model of class lm or set of coefficients.
voxsel	Do Voxel Selection based on normalized T2 (logical)
smooth.using	Character vector to decide if using GaussSmoothArray from AnalyzeFMRI or fslsmooth from fslr package
voxsel.sigma	Sigma passed to voxel_select
voxsel.ksize	Kernel size passed to voxel_select
s.sigma	Sigma passed to GaussSmoothArray
s.ksize	Kernel size passed to GaussSmoothArray
plot.imgs	Plot images along the way
slice	Slice to be plotted
pdfname	Name of pdf created for plot.imgs
verbose	Print Diagnostic Messages

Value

array

See Also

predict

Examples

## Not run: 
download_data()
modes = c("FLAIR", "PD", "T2", "VolumetricT1")
modals = paste0(modes, "norm.nii.gz")
base_files = system.file("01", "Baseline", modals,
package = "sublime")
base_imgs = lapply(base_files, readNIfTI, reorient = FALSE)
f_files = system.file("01", "FollowUp", modals, package="sublime")
f_imgs = lapply(f_files, readNIfTI, reorient=FALSE) 
names(base_imgs) = names(f_imgs) = modes
baseline_nawm_file =  system.file("01", "Baseline", 
"nawm.nii.gz", package="sublime")
baseline_nawm_mask =  readNIfTI(baseline_nawm_file, reorient=FALSE)
baseline_nawm_mask = drop(baseline_nawm_mask)
follow_up_nawm_file =  system.file("01", "FollowUp", 
"nawm.nii.gz", package="sublime")
follow_up_nawm_mask =  readNIfTI(follow_up_nawm_file, reorient=FALSE) 
brain_file =  system.file("01", "duramask.nii.gz", package="sublime")
brain_mask =  readNIfTI(brain_file, reorient=FALSE) 
brain_mask = drop(brain_mask)
on_cran = !identical(Sys.getenv("NOT_CRAN"), "true")
if (on_cran) {
follow_up_nawm_mask = NULL
baseline_nawm_mask = NULL
}
smooth.using = "GaussSmoothArray"
verbose = TRUE
time_diff = 10
voxsel = TRUE
model = sublime_model
#voxsel.sigma = s.sigma =diag(3,3)
#s.ksize = 3
#voxsel.ksize = 5

outimg = SuBLIME_prediction(
baseline_flair = base_imgs[["FLAIR"]],
follow_up_flair= f_imgs[["FLAIR"]],
baseline_pd = base_imgs[["PD"]],
follow_up_pd = f_imgs[["PD"]],
baseline_t2 = base_imgs[["T2"]], 
follow_up_t2 = f_imgs[["T2"]],
baseline_t1 = base_imgs[["VolumetricT1"]],
follow_up_t1 = f_imgs[["VolumetricT1"]],
time_diff = time_diff,
baseline_nawm_mask = baseline_nawm_mask,
brain_mask = brain_mask,
voxsel = voxsel,
model = model, plot.imgs= TRUE,
pdfname = file.path(tempdir(), "pckg_diagnostc.pdf")
)
 
 
nopd_outimg = SuBLIME_prediction(
 baseline_flair = base_imgs[["FLAIR"]],
follow_up_flair= f_imgs[["FLAIR"]],
baseline_pd = NULL,
follow_up_pd = NULL,
baseline_t2 = base_imgs[["T2"]],
follow_up_t2 = f_imgs[["T2"]],
baseline_t1 = base_imgs[["VolumetricT1"]],
follow_up_t1 = f_imgs[["VolumetricT1"]],
time_diff = time_diff,
baseline_nawm_mask = baseline_nawm_mask,
brain_mask = brain_mask,
voxsel = TRUE,
model = sublime::nopd_sublime_model, plot.imgs= TRUE,
pdfname = file.path(tempdir(), "pckg_diagnostc.pdf")
)  
names(base_imgs) = paste0("baseline_", c("flair", "pd", "t2", "t1"))
names(f_imgs) = paste0("follow_up_", c("flair", "pd", "t2", "t1"))
attach(base_imgs)
attach(f_imgs)

## End(Not run)

neuroconductor-devel/sublime documentation built on July 3, 2025, 11:54 p.m.