import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
from deepneuro.data.data_collection import DataCollection
from deepneuro.augmentation.augment import ExtractPatches
from deepneuro.postprocessing.label import BinarizeLabel
from deepneuro.preprocessing.signal import ZeroMeanNormalization
from deepneuro.models.weighted_cat_cross_entropy import WeightedCategoricalCrossEntropy
TrainingDataCollection = DataCollection(data_sources={'csv': 'Metastases_Data_Train.csv'})
TestingDataCollection = DataCollection(data_sources={'csv': 'Metastases_Data_Test.csv'})
Normalization = ZeroMeanNormalization(data_groups=['input_data'])
TrainingDataCollection.append_preprocessor(Normalization)
def BrainRegion(data):
return data['input_data'] != 0
def TumorRegion(data):
return data['ground_truth'] == 1
PatchAugmentation = ExtractPatches(patch_shape=(32, 32, 32),
patch_region_conditions=[[BrainRegion, 0.70], [TumorRegion, 0.30]])
TrainingDataCollection.append_augmentation(PatchAugmentation, multiplier=20)
TrainingDataCollection.write_data_to_file('training_data.hdf5')
ModelParameters = {'input_shape': (32, 32, 32, 1),
'cost_function': 'weighted_categorical_label',
''}
UNETModel = UNet(**ModelParameters)
def skull_strip(output_folder, T1POST=None, FLAIR=None, ground_truth=None, input_directory=None, bias_corrected=True, resampled=False, registered=False, normalized=False, preprocessed=False, save_preprocess=False, save_all_steps=False, mask_output='skullstrip_mask.nii.gz', verbose=True):
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
input_data = {'input_modalities': [FLAIR, T1POST]}
if ground_truth is not None:
input_data['ground_truth'] = [ground_truth]
if input_directory is None:
if any(data is None for data in input_data):
raise ValueError("Cannot segment GBM. Please specify all four modalities.")
data_collection = DataCollection(verbose=verbose)
data_collection.add_case(input_data, case_name=output_folder)
else:
data_collection = DataCollection(input_directory, modality_dict=input_data, verbose=verbose)
data_collection.fill_data_groups()
#--------------------------------------------------------------------#
# Step 2, Preprocess Data
#--------------------------------------------------------------------#
if not preprocessed:
print 'ABOUT TO PREPROCESS....'
# Random hack to save DICOMs to niftis for further processing.
preprocessing_steps = [Preprocessor(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not bias_corrected:
preprocessing_steps += [N4BiasCorrection(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not resampled:
preprocessing_steps += [Resample(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not registered:
preprocessing_steps += [Coregister(data_groups=['input_modalities'], save_output=(save_preprocess or save_all_steps), verbose=verbose, output_folder=output_folder, reference_channel=0)]
if not normalized:
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, preprocessor_string='_preprocessed')]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 3, Skullstripping
#--------------------------------------------------------------------#
skullstrip_prediction_parameters = {'inputs': ['input_modalities'],
'output_filename': os.path.join(output_folder, mask_output),
'batch_size': 25,
'patch_overlaps': 8,
'channels_first': True,
'patch_dimensions': [-3, -2, -1],
'output_patch_shape': (1, 64, 64, 32),
# 'input_channels': [0, 3],
}
skull_stripping_model = load_old_model(load('Skull_Strip_T1Post_FLAIR'))
skull_stripping_prediction = ModelPatchesInference(**skullstrip_prediction_parameters)
label_binarization = BinarizeLabel()
largest_component = LargestComponents()
hole_filler = FillHoles(postprocessor_string='_label')
skull_stripping_prediction.append_postprocessor([label_binarization, largest_component, hole_filler])
skull_stripping_model.append_output([skull_stripping_prediction])
for case in data_collection.cases:
print '\nStarting New Case...\n'
skull_stripping_prediction.case = case
skull_stripping_mask = skull_stripping_model.generate_outputs(data_collection)[0]['filenames'][-1]
if not save_preprocess:
for index, file in enumerate(data_collection.data_groups['input_modalities'].preprocessed_case):
os.remove(file)
def predict_GBM(output_folder,
T2=None,
T1=None,
T1POST=None,
FLAIR=None,
ground_truth=None,
input_directory=None,
bias_corrected=True,
resampled=False,
registered=False,
skullstripped=False,
normalized=False,
preprocessed=False,
save_preprocess=False,
save_all_steps=False,
output_wholetumor_filename='wholetumor_segmentation.nii.gz',
output_enhancing_filename='enhancing_segmentation.nii.gz',
verbose=True):
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
input_data = {'input_modalities': [FLAIR, T2, T1, T1POST]}
if ground_truth is not None:
input_data['ground_truth'] = [ground_truth]
if input_directory is None:
if any(data is None for data in input_data):
raise ValueError(
"Cannot segment GBM. Please specify all four modalities.")
data_collection = DataCollection(verbose=verbose)
data_collection.add_case(input_data, case_name=output_folder)
else:
data_collection = DataCollection(input_directory,
modality_dict=input_data,
verbose=verbose)
data_collection.fill_data_groups()
#--------------------------------------------------------------------#
# Step 2, Preprocess Data
#--------------------------------------------------------------------#
if not preprocessed or True:
# Random hack to save DICOMs to niftis for further processing.
preprocessing_steps = [
DICOMConverter(data_groups=['input_modalities'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not bias_corrected:
preprocessing_steps += [
N4BiasCorrection(data_groups=['input_modalities'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not resampled:
preprocessing_steps += [
Resample(data_groups=['input_modalities'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not registered:
preprocessing_steps += [
Coregister(data_groups=['input_modalities'],
save_output=(save_preprocess or save_all_steps),
verbose=verbose,
output_folder=output_folder,
reference_channel=1)
]
if not skullstripped:
preprocessing_steps += [
SkullStrip(data_groups=['input_modalities'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
reference_channel=1)
]
if not normalized:
preprocessing_steps += [
ZeroMeanNormalization(
data_groups=['input_modalities'],
save_output=save_all_steps,
verbose=verbose,
mask_preprocessor=preprocessing_steps[-1],
preprocessor_string='_preprocessed')
]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 3, Segmentation
#--------------------------------------------------------------------#
wholetumor_prediction_parameters = {
'inputs': ['input_modalities'],
'output_filename': os.path.join(output_folder,
output_wholetumor_filename),
'batch_size': 75,
'patch_overlaps': 1,
'channels_first': True,
'patch_dimensions': [-3, -2, -1],
'output_patch_shape': (1, 26, 26, 26),
# 'input_channels': [0, 3],
}
enhancing_prediction_parameters = {
'inputs': ['input_modalities'],
'output_filename': os.path.join(output_folder,
output_enhancing_filename),
'batch_size': 75,
'patch_overlaps': 1,
'channels_first': True,
'output_patch_shape': (1, 26, 26, 26),
'patch_dimensions': [-3, -2, -1]
}
wholetumor_model = load_old_model(load('Segment_GBM_wholetumor'))
enhancing_model = load_old_model(load('Segment_GBM_enhancing'))
wholetumor_prediction = ModelPatchesInference(
**wholetumor_prediction_parameters)
wholetumor_model.append_output([wholetumor_prediction])
enhancing_prediction = ModelPatchesInference(
**enhancing_prediction_parameters)
enhancing_model.append_output([enhancing_prediction])
label_binarization = BinarizeLabel(postprocessor_string='_label')
wholetumor_prediction.append_postprocessor([label_binarization])
enhancing_prediction.append_postprocessor([label_binarization])
for case in data_collection.cases:
print '\nStarting New Case...\n'
wholetumor_file = wholetumor_model.generate_outputs(
data_collection, case)[0]['filenames'][-1]
data_collection.add_channel(case, wholetumor_file)
enhancing_file = enhancing_model.generate_outputs(
data_collection, case)[0]['filenames'][-1]
data_collection.clear_outputs()