def predict_ischemic_stroke(output_folder, B0, DWI, ground_truth=None, input_directory=None, bias_corrected=True, resampled=False, registered=False, normalized=False, preprocessed=False, save_preprocess=False, save_all_steps=False, output_segmentation_filename='segmentation.nii.gz', verbose=True, input_data=None, registration_reference='FLAIR'):
registration_reference_channel = 1
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[B0, DWI], output_folder=output_folder, input_directory=input_directory, ground_truth=ground_truth, input_data=input_data, verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models
#--------------------------------------------------------------------#
stroke_prediction_parameters = {'inputs': ['input_data'],
'output_filename': os.path.join(output_folder, output_segmentation_filename),
'batch_size': 50,
'patch_overlaps': 8,
'output_patch_shape': (62, 62, 6, 1)}
stroke_model = load_model_with_output(model_name='ischemic_stroke', outputs=[ModelPatchesInference(**stroke_prediction_parameters)], postprocessors=[BinarizeLabel(postprocessor_string='_label')])
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
preprocessing_steps = [DICOMConverter(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not registered:
preprocessing_steps += [Coregister(data_groups=['input_data'], save_output=(save_preprocess or save_all_steps), verbose=verbose, output_folder=output_folder, reference_channel=registration_reference_channel)]
if not normalized:
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, preprocessor_string='_preprocessed')]
else:
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, mask_zeros=True, preprocessor_string='_preprocessed')]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
for case in data_collection.cases:
docker_print('Starting New Case...')
docker_print('Ischemic Stroke Prediction')
docker_print('======================')
stroke_model.generate_outputs(data_collection, case)[0]['filenames'][-1]
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,
T1POST=None,
FLAIR=None,
T1PRE=None,
ground_truth=None,
input_directory=None,
bias_corrected=True,
resampled=False,
registered=False,
skullstripped=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,
input_data=None):
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[FLAIR, T1POST, T1PRE],
output_folder=output_folder,
input_directory=input_directory,
ground_truth=ground_truth,
input_data=input_data,
verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models
#--------------------------------------------------------------------#
wholetumor_prediction_parameters = {
'inputs': ['input_data'],
'output_filename': os.path.join(output_folder,
output_wholetumor_filename),
'batch_size': 50,
'patch_overlaps': 8,
'output_patch_shape': (56, 56, 6, 1),
'input_channels': [0, 1]
}
enhancing_prediction_parameters = {
'inputs': ['input_data'],
'output_filename': os.path.join(output_folder,
output_enhancing_filename),
'batch_size': 50,
'patch_overlaps': 8,
'output_patch_shape': (56, 56, 6, 1)
}
wholetumor_model = load_model_with_output(
model_name='gbm_wholetumor_mri',
outputs=[ModelPatchesInference(**wholetumor_prediction_parameters)],
postprocessors=[BinarizeLabel(postprocessor_string='_label')])
enhancing_model = load_model_with_output(
model_name='gbm_enhancingtumor_mri',
outputs=[ModelPatchesInference(**enhancing_prediction_parameters)],
postprocessors=[BinarizeLabel(postprocessor_string='_label')])
if not preprocessed and not skullstripped:
skullstripping_prediction_parameters = {
'inputs': ['input_data'],
'output_filename':
os.path.join(output_folder, 'skullstrip_mask.nii.gz'),
'batch_size':
50,
'patch_overlaps':
3,
'output_patch_shape': (56, 56, 6, 1),
'save_to_file':
False
}
skullstripping_model = load_model_with_output(
model_name='skullstrip_mri',
outputs=[
ModelPatchesInference(**skullstripping_prediction_parameters)
],
postprocessors=[BinarizeLabel(),
FillHoles(),
LargestComponents()])
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
# Random hack to save DICOMs to niftis for further processing.
preprocessing_steps = [
DICOMConverter(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not bias_corrected:
preprocessing_steps += [
N4BiasCorrection(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not registered:
preprocessing_steps += [
Coregister(data_groups=['input_data'],
save_output=(save_preprocess or save_all_steps),
verbose=verbose,
output_folder=output_folder,
reference_channel=0)
]
if not skullstripped:
preprocessing_steps += [
ZeroMeanNormalization(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
preprocessing_steps += [
SkullStrip_Model(data_groups=['input_data'],
model=skullstripping_model,
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
reference_channel=[0, 1])
]
preprocessing_steps += [
ZeroMeanNormalization(
data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
mask_preprocessor=preprocessing_steps[-1],
preprocessor_string='_preprocessed')
]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
for case in data_collection.cases:
docker_print('\nStarting New Case...\n')
docker_print('Whole Tumor Prediction')
docker_print('======================')
wholetumor_file = wholetumor_model.generate_outputs(
data_collection, case)[0]['filenames'][-1]
data_collection.add_channel(case, wholetumor_file)
docker_print('Enhancing Tumor Prediction')
docker_print('======================')
enhancing_model.generate_outputs(data_collection, case)
data_collection.clear_outputs()
def predict_ischemic_stroke(output_folder,
B0,
DWI,
ground_truth=None,
input_directory=None,
registered=False,
preprocessed=False,
save_only_segmentations=False,
save_all_steps=False,
output_segmentation_filename='segmentation.nii.gz',
input_data=None,
registration_reference='FLAIR',
quiet=False):
verbose = not quiet
save_preprocessed = not save_only_segmentations
registration_reference_channel = 1
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[B0, DWI], output_folder=output_folder, input_directory=input_directory, ground_truth=ground_truth, input_data=input_data, verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models
#--------------------------------------------------------------------#
stroke_prediction_parameters = {'inputs': ['input_data'],
'output_directory': output_folder,
'output_filename': output_segmentation_filename,
'batch_size': 50,
'patch_overlaps': 6,
'output_patch_shape': (62, 62, 6, 1),
'case_in_filename': False,
'verbose': verbose}
stroke_model = load_model_with_output(model_name='ischemic_stroke', outputs=[PatchesInference(**stroke_prediction_parameters)], postprocessors=[BinarizeLabel(postprocessor_string='label')])
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
preprocessing_steps = [DICOMConverter(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not registered:
preprocessing_steps += [Coregister(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, reference_channel=registration_reference_channel)]
if not preprocessed:
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_preprocessed, verbose=verbose, output_folder=output_folder, mask_zeros=True, preprocessor_string='_preprocessed')]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
if verbose:
docker_print('Starting New Case...')
docker_print('Ischemic Stroke Prediction')
docker_print('======================')
stroke_model.generate_outputs(data_collection, output_folder)
data_collection.clear_preprocessor_outputs()
def predict_GBM(output_folder,
T1POST=None,
FLAIR=None,
T1PRE=None,
ground_truth=None,
input_directory=None,
bias_corrected=True,
resampled=False,
registered=False,
skullstripped=False,
preprocessed=False,
save_only_segmentations=False,
save_all_steps=False,
output_wholetumor_filename='wholetumor_segmentation.nii.gz',
output_enhancing_filename='enhancing_segmentation.nii.gz',
output_probabilities=False,
quiet=False,
input_data=None,
registration_reference='FLAIR'):
verbose = not quiet
save_preprocessed = not save_only_segmentations
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[FLAIR, T1POST, T1PRE], output_folder=output_folder, input_directory=input_directory, ground_truth=ground_truth, input_data=input_data, verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models and Postprocessors
#--------------------------------------------------------------------#
wholetumor_prediction_parameters = {'output_directory': output_folder,
'output_filename': output_wholetumor_filename,
'batch_size': 50,
'patch_overlaps': 6,
'output_patch_shape': (56, 56, 6, 1),
'case_in_filename': False,
'verbose': verbose}
enhancing_prediction_parameters = {'output_directory': output_folder,
'output_filename': output_enhancing_filename,
'batch_size': 50,
'patch_overlaps': 6,
'output_patch_shape': (56, 56, 6, 1),
'case_in_filename': False,
'verbose': verbose}
wholetumor_model = load_model_with_output(model_name='gbm_wholetumor_mri',
outputs=[PatchesInference(**wholetumor_prediction_parameters)],
postprocessors=[BinarizeLabel(postprocessor_string='label')])
enhancing_model = load_model_with_output(model_name='gbm_enhancingtumor_mri',
outputs=[PatchesInference(**enhancing_prediction_parameters)],
postprocessors=[BinarizeLabel(postprocessor_string='label')])
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
preprocessing_steps = [DICOMConverter(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not bias_corrected:
preprocessing_steps += [N4BiasCorrection(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
if not registered:
preprocessing_steps += [Coregister(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, reference_channel=0)]
if not skullstripped:
skullstripping_prediction_parameters = {'inputs': ['input_data'],
'output_filename': os.path.join(output_folder, 'skullstrip_mask.nii.gz'),
'batch_size': 50,
'patch_overlaps': 3,
'output_patch_shape': (56, 56, 6, 1),
'save_to_file': False,
'data_collection': data_collection}
skullstripping_model = load_model_with_output(model_name='skullstrip_mri', outputs=[PatchesInference(**skullstripping_prediction_parameters)], postprocessors=[BinarizeLabel(), FillHoles(), LargestComponents()])
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)]
preprocessing_steps += [SkullStrip_Model(data_groups=['input_data'], model=skullstripping_model, save_output=save_all_steps, verbose=verbose, output_folder=output_folder, reference_channel=[0, 1])]
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_preprocessed, verbose=verbose, output_folder=output_folder, mask_preprocessor=preprocessing_steps[-1], preprocessor_string='_preprocessed')]
else:
preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_data'], save_output=save_preprocessed, verbose=verbose, output_folder=output_folder, mask_zeros=True, preprocessor_string='_preprocessed')]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
if verbose:
docker_print('Starting New Case...')
docker_print('Whole Tumor Prediction')
docker_print('======================')
wholetumor_file = wholetumor_model.generate_outputs(data_collection, output_folder)[0]['filenames'][-1]
data_collection.add_channel(output_folder, wholetumor_file)
if verbose:
docker_print('Enhancing Tumor Prediction')
docker_print('======================')
enhancing_model.generate_outputs(data_collection, output_folder)
data_collection.clear_preprocessor_outputs()
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',
input_data=None,
verbose=True):
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[FLAIR, T1POST],
output_folder=output_folder,
input_directory=input_directory,
ground_truth=ground_truth,
input_data=input_data,
verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models
#--------------------------------------------------------------------#
skullstripping_prediction_parameters = {
'inputs': ['input_data'],
'output_filename': os.path.join(output_folder, mask_output),
'batch_size': 50,
'patch_overlaps': 6,
'channels_first': False,
'patch_dimensions': [-4, -3, -2],
'output_patch_shape': (56, 56, 6, 1)
}
skullstripping_model = load_model_with_output(
model_name='skullstrip_mri',
outputs=[
ModelPatchesInference(**skullstripping_prediction_parameters)
],
postprocessors=[
BinarizeLabel(),
FillHoles(),
LargestComponents(postprocessor_string='_label')
])
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
# Random hack to save DICOMs to niftis for further processing.
preprocessing_steps = [
DICOMConverter(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not bias_corrected:
preprocessing_steps += [
N4BiasCorrection(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not registered:
preprocessing_steps += [
Coregister(data_groups=['input_data'],
save_output=(save_preprocess or save_all_steps),
verbose=verbose,
output_folder=output_folder,
reference_channel=0)
]
preprocessing_steps += [
ZeroMeanNormalization(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
for case in data_collection.cases:
docker_print('\nStarting New Case...\n')
docker_print('Skullstripping Prediction')
docker_print('======================')
skullstripping_model.generate_outputs(data_collection, case)
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()
def predict_brain_mets(output_folder,
T2=None,
T1POST=None,
T1PRE=None,
FLAIR=None,
ground_truth=None,
input_directory=None,
bias_corrected=True,
resampled=False,
registered=False,
skullstripped=False,
preprocessed=False,
save_preprocess=False,
save_all_steps=False,
output_segmentation_filename='segmentation.nii.gz',
verbose=True,
input_data=None,
registration_reference='FLAIR'):
registration_reference_channel = 1
#--------------------------------------------------------------------#
# Step 1, Load Data
#--------------------------------------------------------------------#
data_collection = load_data(inputs=[T1PRE, T1POST, T2, FLAIR],
output_folder=output_folder,
input_directory=input_directory,
ground_truth=ground_truth,
input_data=input_data,
verbose=verbose)
#--------------------------------------------------------------------#
# Step 2, Load Models
#--------------------------------------------------------------------#
mets_prediction_parameters = {
'inputs': ['input_data'],
'output_filename':
os.path.join(output_folder, output_segmentation_filename),
'batch_size':
50,
'patch_overlaps':
8,
'output_patch_shape': (28, 28, 28, 1),
'output_channels': [1]
}
mets_model = load_model_with_output(
model_name='mets_enhancing',
outputs=[ModelPatchesInference(**mets_prediction_parameters)],
postprocessors=[BinarizeLabel(postprocessor_string='_label')],
wcc_weights={
0: 0.1,
1: 3.0
})
#--------------------------------------------------------------------#
# Step 3, Add Data Preprocessors
#--------------------------------------------------------------------#
if not preprocessed:
# Random hack to save DICOMs to niftis for further processing.
preprocessing_steps = [
DICOMConverter(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not skullstripped:
skullstripping_prediction_parameters = {
'inputs': ['input_data'],
'output_filename':
os.path.join(output_folder, 'skullstrip_mask.nii.gz'),
'batch_size':
50,
'patch_overlaps':
3,
'output_patch_shape': (56, 56, 6, 1),
'save_to_file':
False,
'data_collection':
data_collection
}
skullstripping_model = load_model_with_output(
model_name='skullstrip_mri',
outputs=[
ModelPatchesInference(
**skullstripping_prediction_parameters)
],
postprocessors=[
BinarizeLabel(),
FillHoles(),
LargestComponents()
])
if not bias_corrected:
preprocessing_steps += [
N4BiasCorrection(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
if not registered:
preprocessing_steps += [
Coregister(data_groups=['input_data'],
save_output=(save_preprocess or save_all_steps),
verbose=verbose,
output_folder=output_folder,
reference_channel=registration_reference_channel)
]
if not skullstripped:
preprocessing_steps += [
ZeroMeanNormalization(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder)
]
preprocessing_steps += [
SkullStrip_Model(data_groups=['input_data'],
model=skullstripping_model,
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
reference_channel=[3, 1])
]
preprocessing_steps += [
ZeroMeanNormalization(
data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
mask_preprocessor=preprocessing_steps[-1],
preprocessor_string='_preprocessed')
]
else:
preprocessing_steps += [
ZeroMeanNormalization(data_groups=['input_data'],
save_output=save_all_steps,
verbose=verbose,
output_folder=output_folder,
mask_zeros=True,
preprocessor_string='_preprocessed')
]
data_collection.append_preprocessor(preprocessing_steps)
#--------------------------------------------------------------------#
# Step 4, Run Inference
#--------------------------------------------------------------------#
for case in data_collection.cases:
docker_print('Starting New Case...')
docker_print('Enhancing Mets Prediction')
docker_print('======================')
mets_model.generate_outputs(data_collection, case)[0]['filenames'][-1]