This one does fully convolutional inference. No sliding window

applies removing of all but the largest connected component to all niftis in a folder :param min_valid_object_size: :param min_valid_obje

ref and gt are binary integer numpy.ndarray s spacing is assumed to be (1, 1, 1) :param ref: :param pred: :return:

ref and gt are binary integer numpy.ndarray s :param ref: :param gt: :return:

Used to determine the postprocessing for an experiment after all five folds have been completed. In the validation of each fold, the postproc

Construct label set from unique entries in segmentations.

reads all prediction files (nifti) in the input folder, converts the labels back to BraTS convention and saves the result in output_folder

stolen and adapted from here: https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html :param num_iters: :param in

create self.output_folder modify self.output_folder if you are doing cross-validation (one folder per fold) set self.tr_gen

inference_apply_nonlin to sigmoid

Returns the shape of tensor or variable as a tuple of int or None entries. # Arguments x: Tensor or variable. # Returns A tupl

used for if you need to ensemble the five models of a cross-validation. This will restore the model from the checkpoint in fold 0, load all p

pads shape so that it is divisibly by must_be_divisible_by :param shape: :param must_be_divisible_by: :return:

here we use the standard naming scheme to generate list_of_lists and output_files needed by predict_cases :param model: :param input

data is (metrics x experiments x cases) :param data: :return:

This is a utility function to load any nnUNet trainer from a pkl. It will recursively search nnunet.trainig.network_training for the file tha

if debug=True then the temporary files generated for postprocessing determination will be kept

Args: backbone_name: (str) look at list of available backbones. input_shape: (tuple) dimensions of input data (H, W, C)

Args: backbone_name: (str) look at list of available backbones. input_shape: (tuple) dimensions of input data (H, W, C)

Args: backbone_name: (str) look at list of available backbones. input_shape: (tuple) dimensions of input data (H, W, C)

TODO

The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3,

The conv block is the block that has conv layer at shortcut. # Arguments filters: integer, used for first and second conv layers, third co

Help extract intermediate layer outputs from model Args: model: Keras `Model` layer: list of integers/str, list of layers ind

use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation :param dataloader_train: :param data

Computation of input patch size starts out with the new median shape (in voxels) of a dataset. This is opposed to prior experiments w

Permutes axes in a tensor. # Arguments x: Tensor or variable. pattern: A tuple of dimension indices, e.g. `(0, 2, 1)`.

TP / (TP + FP)

Use this function to predict a 3D image. It does not matter whether the network is a 2D or 3D U-Net, it will detect that automaticall

Used to predict new unseen data. Not used for the preprocessing of the training/test data :param input_files: :return: