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 train_Segment_GBM(data_directory, val_data_directory):
# Define input modalities to load.
training_modality_dict = {
'input_modalities':
['FLAIR_pp.*', 'T2_pp.*', 'T1_pp.*', 'T1post_pp.*'],
'ground_truth': ['enhancingmask_pp.nii.gz']
}
load_data = False
train_model = False
load_test_data = False
predict = True
training_data = '/mnt/jk489/QTIM_Databank/DeepNeuro_Datasets/BRATS_enhancing_prediction_only_data.h5'
model_file = '/mnt/jk489/QTIM_Databank/DeepNeuro_Datasets/BRATS_enhancing_prediction_only_model.h5'
testing_data = '/mnt/jk489/QTIM_Databank/DeepNeuro_Datasets/BRATS_enhancing_prediction_only_data.h5'
# Write the data to hdf5
if (not os.path.exists(training_data) and train_model) or load_data:
# Create a Data Collection
training_data_collection = DataCollection(
data_directory, modality_dict=training_modality_dict, verbose=True)
training_data_collection.fill_data_groups()
# Define patch sampling regions
def brain_region(data):
return (data['ground_truth'] != 1) & (data['input_modalities'] !=
0)
def roi_region(data):
return data['ground_truth'] == 1
def empty_region(data):
return data['input_modalities'] == 0
# Add patch augmentation
patch_augmentation = ExtractPatches(
patch_shape=(32, 32, 32),
patch_region_conditions=[[empty_region, .05], [brain_region, .25],
[roi_region, .7]],
data_groups=['input_modalities', 'ground_truth'],
patch_dimensions={
'ground_truth': [1, 2, 3],
'input_modalities': [1, 2, 3]
})
training_data_collection.append_augmentation(patch_augmentation,
multiplier=2000)
# Write data to hdf5
training_data_collection.write_data_to_file(training_data)
if train_model:
# Or load pre-loaded data.
training_data_collection = DataCollection(data_storage=training_data,
verbose=True)
training_data_collection.fill_data_groups()
# Add left-right flips
flip_augmentation = Flip_Rotate_2D(
flip=True,
rotate=False,
data_groups=['input_modalities', 'ground_truth'])
# flip_augmentation = Flip_Rotate_3D(data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(flip_augmentation,
multiplier=2)
# Define model parameters
model_parameters = {
'input_shape': (32, 32, 32, 4),
'downsize_filters_factor': 1,
'pool_size': (2, 2, 2),
'filter_shape': (5, 5, 5),
'dropout': 0,
'batch_norm': True,
'initial_learning_rate': 0.000001,
'output_type': 'regression',
'num_outputs': 1,
'activation': 'relu',
'padding': 'same',
'implementation': 'keras',
'depth': 4,
'max_filter': 512
}
# Create U-Net
unet_model = UNet(**model_parameters)
plot_model(unet_model.model,
to_file='model_image_dn.png',
show_shapes=True)
training_parameters = {
'input_groups': ['input_modalities', 'ground_truth'],
'output_model_filepath': model_file,
'training_batch_size': 64,
'num_epochs': 1000,
'training_steps_per_epoch': 20
}
unet_model.train(training_data_collection, **training_parameters)
else:
unet_model = load_old_model(model_file)
# Define input modalities to load.
testing_modality_dict = {
'input_modalities':
['FLAIR_pp.*', 'T2_pp.*', 'T1_pp.*', 'T1post_pp.*']
}
if predict:
testing_data_collection = DataCollection(
val_data_directory,
modality_dict=testing_modality_dict,
verbose=True)
testing_data_collection.fill_data_groups()
if load_test_data:
# Write data to hdf5
testing_data_collection.write_data_to_file(testing_data)
testing_parameters = {
'inputs': ['input_modalities'],
'output_filename': 'brats_enhancing_only_prediction.nii.gz',
'batch_size': 250,
'patch_overlaps': 1,
'output_patch_shape': (26, 26, 26, 4),
'save_all_steps': True
}
prediction = ModelPatchesInference(**testing_parameters)
label_binarization = BinarizeLabel(postprocessor_string='_label')
prediction.append_postprocessor([label_binarization, largest_island])
unet_model.append_output([prediction])
unet_model.generate_outputs(testing_data_collection)
def train_Segment_GBM(data_directory, val_data_directory):
# Define input modalities to load.
training_modality_dict = {
'input_modalities': [
'*FLAIR*', ['*T2SPACE*', '*T2_pp*'], ['*T1_pp.*', '*MPRAGE_Pre*'],
['*T1post_pp.*', '*MPRAGE_POST*'], ['enhancing*'],
['wholetumor*', 'full_edemamask*']
],
'ground_truth': [['enhancing*'], ['wholetumor*', 'full_edemamask*']]
}
load_data = False
train_model = False
load_test_data = True
predict = True
training_data = '/mnt/jk489/QTIM_Databank/DeepNeuro_Datasets/enhancing_label_upsampling_323232.h5'
model_file = 'label_upsampling_323232_model_correct.h5'
testing_data = './FLAIR_upsampling_323232_test.h5'
# Write the data to hdf5
if (not os.path.exists(training_data) and train_model) or load_data:
# Create a Data Collection
training_data_collection = DataCollection(
data_directory, modality_dict=training_modality_dict, verbose=True)
training_data_collection.fill_data_groups()
# Define patch sampling regions
def brain_region(data):
return (data['ground_truth'] != 1) & (data['input_modalities'] !=
0)
def roi_region(data):
return data['ground_truth'] == 1
# Add patch augmentation
patch_augmentation = ExtractPatches(
patch_shape=(32, 32, 32),
patch_region_conditions=[[roi_region, 1]],
data_groups=['input_modalities', 'ground_truth'],
patch_dimensions={
'ground_truth': [0, 1, 2],
'input_modalities': [0, 1, 2]
})
training_data_collection.append_augmentation(patch_augmentation,
multiplier=70)
# Write data to hdf5
training_data_collection.write_data_to_file(training_data)
if train_model:
# Or load pre-loaded data.
training_data_collection = DataCollection(data_storage=training_data,
verbose=True)
training_data_collection.fill_data_groups()
# Choose a modality
choice_augmentation = ChooseData(
axis={
'input_modalities': -1,
'ground_truth': -1
},
choices=[-1, -2],
data_groups=['input_modalities', 'ground_truth'],
random_sample=False)
training_data_collection.append_augmentation(choice_augmentation,
multiplier=2)
# Add down-sampling
mask_augmentation = Downsample(channel=4,
axes={'input_modalities': [-4, -3, -2]},
factor=3,
data_groups=['input_modalities'])
training_data_collection.append_augmentation(mask_augmentation,
multiplier=4)
# Add left-right flips
flip_augmentation = Flip_Rotate_2D(
flip=True,
rotate=False,
data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(flip_augmentation,
multiplier=2)
# Define model parameters
model_parameters = {
'input_shape': (32, 32, 32, 5),
'downsize_filters_factor': 1,
'pool_size': (2, 2, 2),
'filter_shape': (5, 5, 5),
'dropout': 0,
'batch_norm': True,
'initial_learning_rate': 0.000001,
'output_type': 'binary_label',
'num_outputs': 1,
'activation': 'relu',
'padding': 'same',
'implementation': 'keras',
'depth': 4,
'max_filter': 512
}
# Create U-Net
unet_model = UNet(**model_parameters)
plot_model(unet_model.model,
to_file='model_image_dn.png',
show_shapes=True)
training_parameters = {
'input_groups': ['input_modalities', 'ground_truth'],
'output_model_filepath': model_file,
'training_batch_size': 64,
'num_epochs': 1000,
'training_steps_per_epoch': 20
}
unet_model.train(training_data_collection, **training_parameters)
else:
unet_model = load_old_model(model_file)
# Load testing data..
if not os.path.exists(testing_data) or load_test_data:
# Create a Data Collection
testing_data_collection = DataCollection(
val_data_directory,
modality_dict=training_modality_dict,
verbose=True)
testing_data_collection.fill_data_groups()
# Write data to hdf5
testing_data_collection.write_data_to_file(testing_data)
if predict:
testing_data_collection = DataCollection(data_storage=testing_data,
verbose=True)
testing_data_collection.fill_data_groups()
# Choose a modality
choice_augmentation = ChooseData(
axis={
'input_modalities': -1,
'ground_truth': -1
},
choices=[-1, -2],
data_groups=['input_modalities', 'ground_truth'],
random_sample=False)
testing_data_collection.append_augmentation(choice_augmentation,
multiplier=2)
# Add down-sampling
mask_augmentation = Downsample(channel=4,
axes={'input_modalities': [-4, -3, -2]},
factor=3,
data_groups=['input_modalities'],
random_sample=False)
testing_data_collection.append_augmentation(mask_augmentation,
multiplier=3)
testing_parameters = {
'inputs': ['input_modalities'],
'output_filename': 'deepneuro-label.nii.gz',
'batch_size': 250,
'patch_overlaps': 6,
'output_patch_shape': (26, 26, 26, 4)
}
prediction = ModelPatchesInference(testing_data_collection,
**testing_parameters)
unet_model.append_output([prediction])
unet_model.generate_outputs()
def train_Segment_GBM(data_directory, val_data_directory):
# Define input modalities to load.
if True:
training_modality_dict = {
'input_modalities':
['*FLAIR_pp.*', '*T2_pp.*', '*T1_pp.*', '*T1post_pp.*'],
'ground_truth': ['*full_edemamask_pp.*']
}
else:
training_modality_dict = {
'input_modalities': [['*FLAIR_pp.*', 'FLAIR_norm2*'],
['*T1post_pp.*', 'T1post_norm2*']],
'ground_truth': ['*full_edemamask_pp.*', 'FLAIRmask-label.nii.gz']
}
load_data = True
train_model = True
load_test_data = True
predict = True
training_data = './wholetumor_predict_patches_test3.h5'
model_file = 'wholetumor_segnet-58-0.38.h5'
testing_data = './brats_test_case.h5'
# Write the data to hdf5
if (not os.path.exists(training_data) and train_model) or load_data:
# Create a Data Collection
training_data_collection = DataCollection(
data_directory, modality_dict=training_modality_dict, verbose=True)
training_data_collection.fill_data_groups()
# Define patch sampling regions
def brain_region(data):
return (data['ground_truth'] != 1) & (data['input_modalities'] !=
0)
def roi_region(data):
return data['ground_truth'] == 1
# Add patch augmentation
patch_augmentation = ExtractPatches(
patch_shape=(32, 32, 32),
patch_region_conditions=[[brain_region, 1]],
data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(patch_augmentation,
multiplier=200)
# Add left-right flips
flip_augmentation = Flip_Rotate_2D(
flip=True,
rotate=False,
data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(flip_augmentation,
multiplier=2)
# Write data to hdf5
training_data_collection.write_data_to_file(training_data)
# Or load pre-loaded data.
training_data_collection = DataCollection(data_storage=training_data,
verbose=True)
training_data_collection.fill_data_groups()
# Define model parameters
model_parameters = {
'input_shape': (32, 32, 32, 4),
'downsize_filters_factor': 1,
'pool_size': (2, 2, 2),
'filter_shape': (3, 3, 3),
'dropout': 0,
'batch_norm': True,
'initial_learning_rate': 0.000001,
'output_type': 'binary_label',
'num_outputs': 1,
'activation': 'relu',
'padding': 'same',
'implementation': 'keras',
'depth': 4,
'max_filter': 512
}
# Create U-Net
if train_model:
unet_model = UNet(**model_parameters)
plot_model(unet_model.model,
to_file='model_image_dn.png',
show_shapes=True)
training_parameters = {
'input_groups': ['input_modalities', 'ground_truth'],
'output_model_filepath':
'wholetumor_segnet-{epoch:02d}-{loss:.2f}.h5',
'training_batch_size': 2,
'num_epochs': 100,
'training_steps_per_epoch': 200,
'save_best_only': False
}
unet_model.train(training_data_collection, **training_parameters)
else:
unet_model = load_old_model(model_file)
# Load testing data..
if not os.path.exists(testing_data) or load_test_data:
# Create a Data Collection
testing_data_collection = DataCollection(
val_data_directory,
modality_dict=training_modality_dict,
verbose=True)
testing_data_collection.fill_data_groups()
# Write data to hdf5
testing_data_collection.write_data_to_file(testing_data)
if predict:
testing_data_collection = DataCollection(data_storage=testing_data,
verbose=True)
testing_data_collection.fill_data_groups()
testing_parameters = {
'inputs': ['input_modalities'],
'output_filename': 'deepneuro.nii.gz',
'batch_size': 200,
'patch_overlaps': 1
}
prediction = ModelPatchesInference(testing_data_collection,
**testing_parameters)
unet_model.append_output([prediction])
unet_model.generate_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_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 train_Segment_GBM(data_directory, val_data_directory):
# Define input modalities to load.
training_modality_dict = {'input_modalities':
['*FLAIR*nii.gz', ['*T2SPACE*nii.gz'], ['*MPRAGE_POST*nii.gz'], ['*MPRAGE_Pre*nii.gz']],
'ground_truth': ['*SUV_r_T2_raw.nii.gz*']}
load_data = False
train_model = False
load_test_data = True
predict = True
training_data = '/mnt/jk489/QTIM_Databank/DeepNeuro_Datasets/TMZ_4_323232.h5'
model_file = 'TMZ_4_323232_model.h5'
testing_data = './TMZ_4_323232_test.h5'
# Write the data to hdf5
if (not os.path.exists(training_data) and train_model) or load_data:
# Create a Data Collection
training_data_collection = DataCollection(data_directory, modality_dict=training_modality_dict, verbose=True)
training_data_collection.fill_data_groups()
# Define patch sampling regions
def brain_region(data):
return (data['ground_truth'] != 1) & (data['input_modalities'] != 0)
def roi_region(data):
return data['ground_truth'] >= 1.5
# Add patch augmentation
patch_augmentation = ExtractPatches(patch_shape=(32, 32, 32), patch_region_conditions=[[brain_region, .5], [roi_region, .5]], data_groups=['input_modalities', 'ground_truth'], patch_dimensions={'ground_truth': [0,1,2], 'input_modalities': [0,1,2]})
training_data_collection.append_augmentation(patch_augmentation, multiplier=2000)
# Write data to hdf5
training_data_collection.write_data_to_file(training_data)
if train_model:
# Or load pre-loaded data.
training_data_collection = DataCollection(data_storage=training_data, verbose=True)
training_data_collection.fill_data_groups()
# Add left-right flips
flip_augmentation = Flip_Rotate_2D(flip=True, rotate=False, data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(flip_augmentation, multiplier=2)
# Define model parameters
model_parameters = {'input_shape': (32, 32, 32, 4),
'downsize_filters_factor': 1,
'pool_size': (2, 2, 2),
'filter_shape': (5, 5, 5),
'dropout': 0,
'batch_norm': True,
'initial_learning_rate': 0.000001,
'output_type': 'regression',
'num_outputs': 1,
'activation': 'relu',
'padding': 'same',
'implementation': 'keras',
'depth': 4,
'max_filter': 512}
# Create U-Net
unet_model = UNet(**model_parameters)
plot_model(unet_model.model, to_file='model_image_dn.png', show_shapes=True)
training_parameters = {'input_groups': ['input_modalities', 'ground_truth'],
'output_model_filepath': model_file,
'training_batch_size': 64,
'num_epochs': 1000,
'training_steps_per_epoch': 20}
unet_model.train(training_data_collection, **training_parameters)
else:
unet_model = load_old_model(model_file)
# Load testing data..
if not os.path.exists(testing_data) or load_test_data:
# Create a Data Collection
testing_data_collection = DataCollection(val_data_directory, modality_dict=training_modality_dict, verbose=True)
testing_data_collection.fill_data_groups()
# Write data to hdf5
testing_data_collection.write_data_to_file(testing_data)
if predict:
testing_data_collection = DataCollection(data_storage=testing_data, verbose=True)
testing_data_collection.fill_data_groups()
flip_augmentation = Copy(data_groups=['input_modalities', 'ground_truth'])
testing_data_collection.append_augmentation(flip_augmentation, multiplier=1)
testing_parameters = {'inputs': ['input_modalities'],
'output_filename': 'deepneuro_suv_4.nii.gz',
'batch_size': 50,
'patch_overlaps': 6,
'output_patch_shape': (26,26,26,4)}
prediction = ModelPatchesInference(testing_data_collection, **testing_parameters)
unet_model.append_output([prediction])
unet_model.generate_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]
def train_Segment_GBM(data_directory, val_data_directory):
# Define input modalities to load.
training_modality_dict = {
'input_modalities': ['*phantom*'],
'ground_truth': ['*ktrans*']
}
load_data = False
train_model = False
load_test_data = False
predict = True
training_data = './dce_mri_ktrans_training_884_1.h5'
model_file = 'ktrans_net_884_1_3layer_conv_separated_sym.h5'
testing_data = './dce_mri_ktrans_testing_884_1.h5'
# Write the data to hdf5
if (not os.path.exists(training_data) and train_model) or load_data:
# Create a Data Collection
training_data_collection = DataCollection(
data_directory, modality_dict=training_modality_dict, verbose=True)
training_data_collection.fill_data_groups()
# Define patch sampling regions
def brain_region(data):
return (data['ground_truth'] >= .1)
# Add patch augmentation
patch_augmentation = ExtractPatches(
patch_shape=(8, 8, 4),
patch_region_conditions=[[brain_region, 1]],
data_groups=['input_modalities', 'ground_truth'],
patch_dimensions={
'ground_truth': [0, 1, 2],
'input_modalities': [1, 2, 3]
})
training_data_collection.append_augmentation(patch_augmentation,
multiplier=5000)
# Add left-right flips
flip_augmentation = Flip_Rotate_2D(
flip=True,
rotate=False,
data_groups=['input_modalities', 'ground_truth'])
training_data_collection.append_augmentation(flip_augmentation,
multiplier=2)
# Write data to hdf5
training_data_collection.write_data_to_file(training_data)
# Or load pre-loaded data.
training_data_collection = DataCollection(data_storage=training_data,
verbose=True)
training_data_collection.fill_data_groups()
# Define model parameters
model_parameters = {
'input_shape': (65, 8, 8, 4, 1),
'downsize_filters_factor': 4,
'pool_size': (2, 2, 2),
'filter_shape': (3, 3, 3),
'dropout': .1,
'batch_norm': True,
'initial_learning_rate': 0.000001,
'output_type': 'regression',
'num_outputs': 1,
'activation': 'relu',
'padding': 'same',
'implementation': 'keras',
'depth': 1,
'max_filter': 32
}
# Create U-Net
if train_model:
timenet_model = TimeNet(**model_parameters)
plot_model(timenet_model.model,
to_file='timenet_model.png',
show_shapes=True)
training_parameters = {
'input_groups': ['input_modalities', 'ground_truth'],
'output_model_filepath': model_file,
'training_batch_size': 32,
'num_epochs': 100,
'training_steps_per_epoch': 200,
'save_best_only': True
}
timenet_model.train(training_data_collection, **training_parameters)
else:
timenet_model = load_old_model(model_file)
# Load testing data..
if not os.path.exists(testing_data) or load_test_data:
# Create a Data Collection
testing_data_collection = DataCollection(
val_data_directory,
modality_dict=training_modality_dict,
verbose=True)
testing_data_collection.fill_data_groups()
# Write data to hdf5
testing_data_collection.write_data_to_file(testing_data)
if predict:
testing_data_collection = DataCollection(data_storage=testing_data,
verbose=True)
testing_data_collection.fill_data_groups()
testing_parameters = {
'inputs': ['input_modalities'],
'output_filename': 'deepneuro.nii.gz',
'batch_size': 200,
'patch_overlaps': 8,
'output_patch_shape': (6, 6, 2, 1)
}
prediction = ModelPatchesInference(testing_data_collection,
**testing_parameters)
timenet_model.append_output([prediction])
timenet_model.generate_outputs()