#==============================================================================#
# 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.cv3"
path_preds = "predictions.cv3"
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)
Datasets
Models
