def load_model_with_output(model_path=None, model_name=None, outputs=None, postprocessors=None, **kwargs): if model_path is not None: model = load_old_model(model_path, **kwargs) elif model_name is not None: model = load_old_model(load(model_name), **kwargs) else: print('Error. No model provided.') return for output in outputs: model.append_output([output]) for postprocessor in postprocessors: output.append_postprocessor([postprocessor]) return model
def skull_strip(output_folder, T1POST=None, FLAIR=None, ground_truth=None, input_directory=None, bias_corrected=True, resampled=False, registered=False, normalized=False, preprocessed=False, save_preprocess=False, save_all_steps=False, mask_output='skullstrip_mask.nii.gz', verbose=True): #--------------------------------------------------------------------# # Step 1, Load Data #--------------------------------------------------------------------# input_data = {'input_modalities': [FLAIR, T1POST]} if ground_truth is not None: input_data['ground_truth'] = [ground_truth] if input_directory is None: if any(data is None for data in input_data): raise ValueError("Cannot segment GBM. Please specify all four modalities.") data_collection = DataCollection(verbose=verbose) data_collection.add_case(input_data, case_name=output_folder) else: data_collection = DataCollection(input_directory, modality_dict=input_data, verbose=verbose) data_collection.fill_data_groups() #--------------------------------------------------------------------# # Step 2, Preprocess Data #--------------------------------------------------------------------# if not preprocessed: print 'ABOUT TO PREPROCESS....' # Random hack to save DICOMs to niftis for further processing. preprocessing_steps = [Preprocessor(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)] if not bias_corrected: preprocessing_steps += [N4BiasCorrection(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)] if not resampled: preprocessing_steps += [Resample(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder)] if not registered: preprocessing_steps += [Coregister(data_groups=['input_modalities'], save_output=(save_preprocess or save_all_steps), verbose=verbose, output_folder=output_folder, reference_channel=0)] if not normalized: preprocessing_steps += [ZeroMeanNormalization(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, preprocessor_string='_preprocessed')] data_collection.append_preprocessor(preprocessing_steps) #--------------------------------------------------------------------# # Step 3, Skullstripping #--------------------------------------------------------------------# skullstrip_prediction_parameters = {'inputs': ['input_modalities'], 'output_filename': os.path.join(output_folder, mask_output), 'batch_size': 25, 'patch_overlaps': 8, 'channels_first': True, 'patch_dimensions': [-3, -2, -1], 'output_patch_shape': (1, 64, 64, 32), # 'input_channels': [0, 3], } skull_stripping_model = load_old_model(load('Skull_Strip_T1Post_FLAIR')) skull_stripping_prediction = ModelPatchesInference(**skullstrip_prediction_parameters) label_binarization = BinarizeLabel() largest_component = LargestComponents() hole_filler = FillHoles(postprocessor_string='_label') skull_stripping_prediction.append_postprocessor([label_binarization, largest_component, hole_filler]) skull_stripping_model.append_output([skull_stripping_prediction]) for case in data_collection.cases: print '\nStarting New Case...\n' skull_stripping_prediction.case = case skull_stripping_mask = skull_stripping_model.generate_outputs(data_collection)[0]['filenames'][-1] if not save_preprocess: for index, file in enumerate(data_collection.data_groups['input_modalities'].preprocessed_case): os.remove(file)
def predict_GBM(output_folder, T2=None, T1=None, T1POST=None, FLAIR=None, ground_truth=None, input_directory=None, bias_corrected=True, resampled=False, registered=False, skullstripped=False, normalized=False, preprocessed=False, save_preprocess=False, save_all_steps=False, output_wholetumor_filename='wholetumor_segmentation.nii.gz', output_enhancing_filename='enhancing_segmentation.nii.gz', verbose=True): #--------------------------------------------------------------------# # Step 1, Load Data #--------------------------------------------------------------------# input_data = {'input_modalities': [FLAIR, T2, T1, T1POST]} if ground_truth is not None: input_data['ground_truth'] = [ground_truth] if input_directory is None: if any(data is None for data in input_data): raise ValueError( "Cannot segment GBM. Please specify all four modalities.") data_collection = DataCollection(verbose=verbose) data_collection.add_case(input_data, case_name=output_folder) else: data_collection = DataCollection(input_directory, modality_dict=input_data, verbose=verbose) data_collection.fill_data_groups() #--------------------------------------------------------------------# # Step 2, Preprocess Data #--------------------------------------------------------------------# if not preprocessed or True: # Random hack to save DICOMs to niftis for further processing. preprocessing_steps = [ DICOMConverter(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder) ] if not bias_corrected: preprocessing_steps += [ N4BiasCorrection(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder) ] if not resampled: preprocessing_steps += [ Resample(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder) ] if not registered: preprocessing_steps += [ Coregister(data_groups=['input_modalities'], save_output=(save_preprocess or save_all_steps), verbose=verbose, output_folder=output_folder, reference_channel=1) ] if not skullstripped: preprocessing_steps += [ SkullStrip(data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, output_folder=output_folder, reference_channel=1) ] if not normalized: preprocessing_steps += [ ZeroMeanNormalization( data_groups=['input_modalities'], save_output=save_all_steps, verbose=verbose, mask_preprocessor=preprocessing_steps[-1], preprocessor_string='_preprocessed') ] data_collection.append_preprocessor(preprocessing_steps) #--------------------------------------------------------------------# # Step 3, Segmentation #--------------------------------------------------------------------# wholetumor_prediction_parameters = { 'inputs': ['input_modalities'], 'output_filename': os.path.join(output_folder, output_wholetumor_filename), 'batch_size': 75, 'patch_overlaps': 1, 'channels_first': True, 'patch_dimensions': [-3, -2, -1], 'output_patch_shape': (1, 26, 26, 26), # 'input_channels': [0, 3], } enhancing_prediction_parameters = { 'inputs': ['input_modalities'], 'output_filename': os.path.join(output_folder, output_enhancing_filename), 'batch_size': 75, 'patch_overlaps': 1, 'channels_first': True, 'output_patch_shape': (1, 26, 26, 26), 'patch_dimensions': [-3, -2, -1] } wholetumor_model = load_old_model(load('Segment_GBM_wholetumor')) enhancing_model = load_old_model(load('Segment_GBM_enhancing')) wholetumor_prediction = ModelPatchesInference( **wholetumor_prediction_parameters) wholetumor_model.append_output([wholetumor_prediction]) enhancing_prediction = ModelPatchesInference( **enhancing_prediction_parameters) enhancing_model.append_output([enhancing_prediction]) label_binarization = BinarizeLabel(postprocessor_string='_label') wholetumor_prediction.append_postprocessor([label_binarization]) enhancing_prediction.append_postprocessor([label_binarization]) for case in data_collection.cases: print '\nStarting New Case...\n' wholetumor_file = wholetumor_model.generate_outputs( data_collection, case)[0]['filenames'][-1] data_collection.add_channel(case, wholetumor_file) enhancing_file = enhancing_model.generate_outputs( data_collection, case)[0]['filenames'][-1] data_collection.clear_outputs()