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]