This function trains the TAPAS model using all binded subject-level tibbles produced from the tapas_data() function. The TAPAS model is fit and clamp data is calculated. The clamp data contains the predicted threshold when using the 10th and 90th percentile volume from training data.

tapas_train(data, dsc_cutoff = 0.03, verbose = TRUE)



Data resulting from tapas_data(). The data should be a tibble or data.frame containing binded subject data or a list object with subject data in each element. Data from these subjects will be used for model training.


The Sørensen's–Dice coefficient (DSC) value to use as a cutoff for training inclusion. By default 0.03 is used. This must be a single value between 0 and 1. Only training subjects with a subject-specific threshold estimate resulting in Sørensen's–Dice coefficient (DSC) greater than or equal to the dsc_cutoff will be included in training the TAPAS model.


A logical argument to print messages. Set to TRUE by default.


A list with the TAPAS model (tapas_model) of class gam, the group-level threshold, a tibble with the clamp information (clamp_data), and a tibble with the training data. The clamp information contains the TAPAS-predicted smallest and largest threshold to be applied by using estimates related to the volume at the 10th and 90th percentile.


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 = data, dsc_cutoff = 0.03, verbose = TRUE) # The TAPAS GAM model summary(tapas_model$tapas_model) # The threshold that optimizes group-level DSC tapas_model$group_threshold # The lower and upper bound clamps to avoid extrapolation tapas_model$clamp_data # The training data for the TAPAS `mgcv::gam` function tapas_model$train_data }