--- a
+++ b/scripts/cv_analysis/miscnn_k2.py
@@ -0,0 +1,143 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2020 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+import tensorflow as tf
+from miscnn.data_loading.interfaces import NIFTI_interface
+from miscnn import Data_IO, Preprocessor, Data_Augmentation, Neural_Network
+from miscnn.processing.subfunctions import Normalization, Clipping, Resampling
+from miscnn.neural_network.architecture.unet.standard import Architecture
+from miscnn.neural_network.metrics import tversky_crossentropy, dice_soft, \
+                                          dice_crossentropy, tversky_loss
+from miscnn.evaluation.cross_validation import cross_validation
+from tensorflow.keras.callbacks import ReduceLROnPlateau, TensorBoard, \
+                                       EarlyStopping, CSVLogger, ModelCheckpoint
+from miscnn.evaluation.cross_validation import run_fold, load_disk2fold
+import argparse
+import os
+
+#-----------------------------------------------------#
+#                      Argparser                      #
+#-----------------------------------------------------#
+parser = argparse.ArgumentParser(description="Automated COVID-19 Segmentation")
+parser.add_argument("-f", "--fold", help="Cross-validation fold. Range: [0:5]",
+                    required=True, type=int, dest="fold")
+parser.add_argument("-g", "--gpu", help="GPU ID selection for multi cluster",
+                    required=False, type=int, dest="gpu", default=0)
+
+path_eval = "evaluation.cv2"
+path_preds = "predictions.cv2"
+
+args = parser.parse_args()
+fold = args.fold
+fold_subdir = os.path.join(path_eval, "fold_" + str(fold))
+
+#-----------------------------------------------------#
+#      Tensorflow Configuration for GPU Cluster       #
+#-----------------------------------------------------#
+os.environ["CUDA_VISIBLE_DEVICES"] = str(int(args.gpu))
+
+#-----------------------------------------------------#
+#               Setup of MIScnn Pipeline              #
+#-----------------------------------------------------#
+# Initialize Data IO Interface for NIfTI data
+## We are using 4 classes due to [background, lung_left, lung_right, covid-19]
+interface = NIFTI_interface(channels=1, classes=4)
+
+# Create Data IO object to load and write samples in the file structure
+data_io = Data_IO(interface, input_path="data", output_path=path_preds,
+                  delete_batchDir=False)
+
+# Access all available samples in our file structure
+sample_list = data_io.get_indiceslist()
+sample_list.sort()
+
+# Create and configure the Data Augmentation class
+data_aug = Data_Augmentation(cycles=1, scaling=True, rotations=True,
+                             elastic_deform=True, mirror=True,
+                             brightness=True, contrast=True, gamma=True,
+                             gaussian_noise=True)
+
+# Create a clipping Subfunction to the lung window of CTs (-1250 and 250)
+sf_clipping = Clipping(min=-1250, max=250)
+# Create a pixel value normalization Subfunction to scale between 0-255
+sf_normalize = Normalization(mode="grayscale")
+# Create a resampling Subfunction to voxel spacing 1.58 x 1.58 x 2.70
+sf_resample = Resampling((1.58, 1.58, 2.70))
+# Create a pixel value normalization Subfunction for z-score scaling
+sf_zscore = Normalization(mode="z-score")
+
+# Assemble Subfunction classes into a list
+sf = [sf_clipping, sf_normalize, sf_resample, sf_zscore]
+
+# Create and configure the Preprocessor class
+pp = Preprocessor(data_io, data_aug=data_aug, batch_size=2, subfunctions=sf,
+                  prepare_subfunctions=True, prepare_batches=False,
+                  analysis="patchwise-crop", patch_shape=(160, 160, 80),
+                  use_multiprocessing=True)
+# Adjust the patch overlap for predictions
+pp.patchwise_overlap = (80, 80, 30)
+
+# Initialize the Architecture
+unet_standard = Architecture(depth=4, activation="softmax",
+                             batch_normalization=True)
+
+# Create the Neural Network model
+model = Neural_Network(preprocessor=pp, architecture=unet_standard,
+                       loss=tversky_crossentropy,
+                       metrics=[tversky_loss, dice_soft, dice_crossentropy],
+                       batch_queue_size=3, workers=3, learninig_rate=0.001)
+
+# Define Callbacks
+cb_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=15,
+                          verbose=1, mode='min', min_delta=0.0001, cooldown=1,
+                          min_lr=0.00001)
+cb_es = EarlyStopping(monitor="loss", patience=100)
+cb_tb = TensorBoard(log_dir=os.path.join(fold_subdir, "tensorboard"),
+                    histogram_freq=0, write_graph=True, write_images=True)
+cb_cl = CSVLogger(os.path.join(fold_subdir, "logs.csv"), separator=',',
+                  append=True)
+cb_mc = ModelCheckpoint(os.path.join(fold_subdir, "model.best.hdf5"),
+                        monitor="loss", verbose=1,
+                        save_best_only=True, mode="min")
+
+#-----------------------------------------------------#
+#          Run Pipeline for provided CV Fold          #
+#-----------------------------------------------------#
+# Run pipeline for cross-validation fold
+run_fold(fold, model, epochs=1000, iterations=150, evaluation_path=path_eval,
+         draw_figures=True, callbacks=[cb_lr, cb_es, cb_tb, cb_cl, cb_mc],
+         save_models=False)
+
+# Dump latest model to disk
+model.dump(os.path.join(fold_subdir, "model.latest.hdf5"))
+
+#-----------------------------------------------------#
+#           Inference for provided CV Fold            #
+#-----------------------------------------------------#
+# Load best model weights during fitting
+model.load(os.path.join(fold_subdir, "model.best.hdf5"))
+
+# Obtain training and validation data set
+training, validation = load_disk2fold(os.path.join(fold_subdir,
+                                                   "sample_list.json"))
+
+# Compute predictions
+model.predict(validation, return_output=False)