This function wraps tapas_predict() to run in parallel. This function takes
probability maps across subjects and predicts the subject-specific threshold to apply based on the
TAPAS model generated from tapas_train(). The function will return or save a list of objects
for each subject including the TAPAS predicted subject-specific threshold, the lesion mask produced from
applying this threshold, as well as the lesion mask produced from using the group threshold.
tapas_predict_par(cores = 1, pmap, subject_id, model, clamp = TRUE, k = 0, ret = FALSE, outfile = NULL, verbose = TRUE)
| cores | The number of cores to use. This argument controls at most how many child processes will be run simultaneously. The default is set to 1. |
|---|---|
| pmap | A |
| subject_id | A |
| model | The TAPAS model fit from |
| clamp | A |
| k | The minimum number of voxels for a cluster/component. Segmentation clusters of size less than k are removed from the mask, volume estimation, and the Sørensen's–Dice coefficient (DSC) calculation. |
| ret | A |
| outfile | Is set to |
| verbose | A |
A nested list. Each element in the list contains subject-level data returned from tapas_predict().
if (FALSE) { # Data is provided in the rtapas package as arrays. Below we will convert them to nifti objects. # Before we can implement the train_tapas function we have to generate the training data library(oro.nifti) # Create a list of gold standard manual segmentation train_gold_standard_masks = list(gs1 = gs1, gs2 = gs2, gs3 = gs3, gs4 = gs4, gs5 = gs5, gs6 = gs6, gs7 = gs7, gs8 = gs8, gs9 = gs9, gs10 = gs10) # Convert the gold standard masks to nifti objects train_gold_standard_masks = lapply(train_gold_standard_masks, oro.nifti::nifti) # Make a list of the training probability maps train_probability_maps = list(pmap1 = pmap1, pmap2 = pmap2, pmap3 = pmap3, pmap4 = pmap4, pmap5 = pmap5, pmap6 = pmap6, pmap7 = pmap7, pmap8 = pmap8, pmap9 = pmap9, pmap10 = pmap10) # Convert the probability maps to nifti objects train_probability_maps = lapply(train_probability_maps, oro.nifti::nifti) # Make a list of the brain masks train_brain_masks = list(brain_mask1 = brain_mask, brain_mask2 = brain_mask, brain_mask3 = brain_mask, brain_mask4 = brain_mask, brain_mask5 = brain_mask, brain_mask6 = brain_mask, brain_mask7 = brain_mask, brain_mask8 = brain_mask, brain_mask9 = brain_mask, brain_mask10 = brain_mask) # Convert the brain masks to nifti objects train_brain_masks = lapply(train_brain_masks, oro.nifti::nifti) # Specify training IDs train_ids = paste0('subject_', 1:length(train_gold_standard_masks)) # The function below runs on 2 cores. Be sure your machine has 2 cores available or switch to 1. # Run tapas_data_par function # You can also use the tapas_data function and generate each subjects data data = tapas_data_par(cores = 2, thresholds = seq(from = 0, to = 1, by = 0.01), pmap = train_probability_maps, gold_standard = train_gold_standard_masks, mask = train_brain_masks, k = 0, subject_id = train_ids, ret = TRUE, outfile = NULL, verbose = TRUE) # We can now implement the train_tapas function using the data from tapas_data_par tapas_model = tapas_train(data = train_data1, dsc_cutoff = 0.03, verbose = TRUE) # Obtain 2 test subject probability maps test_probability_maps = list(pmap11 = pmap11, pmap12 = pmap12) # Make array objects niftis test_probability_maps = lapply(test_probability_maps, oro.nifti::nifti) # Create a list of testing brain masks test_brain_masks = list(brain_mask11 = brain_mask, brain_mask12 = brain_mask) # Make array objects niftis test_brain_masks = lapply(test_brain_masks, oro.nifti::nifti) # Create a vector of IDs test_ids = paste0('subject_', (10 + 1:length(test_gold_standard_masks))) # Run tapas_predict_par function test_subject_prediction2 = tapas_predict_par(cores = 2, pmap = test_probability_maps, subject_id = test_ids, model = tapas_model, clamp = TRUE, k = 0, ret = TRUE, outfile = NULL, verbose = TRUE) names(test_subject_prediction2) names(test_subject_prediction2[[1]]) test_subject_prediction2[[1]]$subject_threshold #' # Look at TAPAS binary segmentation from applying the TAPAS threshold oro.nifti::image(test_subject_prediction2[[1]]$tapas_binary_mask) # Look at group threshold binary segmentation from applying the group threshold oro.nifti::image(test_subject_prediction2[[1]]$group_binary_threshold) # You can compare with subject 2 by replacing [[1]] with [[2]] }