Source code for WORC.WORCpy27

#!/usr/bin/env python

# Copyright 2017-2018 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import configparser
import fastr
import os
from random import randint
import WORC.addexceptions as WORCexceptions
import WORC.IOparser.config_WORC as config_io
from WORC.tools.Elastix import Elastix
from WORC.tools.Evaluate import Evaluate


[docs]class WORC(object): """ A Workflow for Optimal Radiomics Classification (WORC) object that serves as a pipeline spawner and manager for optimizating radiomics studies. Depending on the attributes set, the object will spawn an appropriate pipeline and manage it. Note that many attributes are lists and can therefore contain multiple instances. For example, when providing two sequences per patient, the "images" list contains two items. The type of items in the lists is described below. All objects that serve as source for your network, i.e. refer to actual files to be used, should be formatted as fastr sources suited for one of the fastr plugings, see also http://fastr.readthedocs.io/en/stable/fastr.reference.html#ioplugin-reference The objects should be lists of these fastr sources or dictionaries with the sample ID's, e.g. images_train = [{'Patient001': vfs://input/CT001.nii.gz, 'Patient002': vfs://input/CT002.nii.gz}, {'Patient001': vfs://input/MR001.nii.gz, 'Patient002': vfs://input/MR002.nii.gz}] Attributes ------------------ name: String, default 'WORC' name of the network. configs: list, required Configuration parameters, either ConfigParser objects created through the defaultconfig function or paths of config .ini files. (list, required) labels: list, required Paths to files containing patient labels (.txt files). network: automatically generated The FASTR network generated through the "build" function. images: list, optional Paths refering to the images used for Radiomics computation. Images should be of the ITK Image type. segmentations: list, optional Paths refering to the segmentations used for Radiomics computation. Segmentations should be of the ITK Image type. semantics: semantic features per image type (list, optional) masks: state which pixels of images are valid (list, optional) features: input Radiomics features for classification (list, optional) metadata: DICOM headers belonging to images (list, optional) Elastix_Para: parameter files for Elastix (list, optional) fastr_plugin: plugin to use for FASTR execution fastr_tempdir: temporary directory to use for FASTR execution additions: additional inputs for your network (dict, optional) source_data: data to use as sources for FASTR (dict) sink_data: data to use as sinks for FASTR (dict) CopyMetadata: Boolean, default True when using elastix, copy metadata from image to segmentation or not """
[docs] def __init__(self, name='WORC'): """Initialize WORC object. Set the initial variables all to None, except for some defaults. Arguments: name: name of the nework (string, optional) """ self.name = name # Initialize several objects self.configs = list() self.fastrconfigs = list() self.images_train = list() self.segmentations_train = list() self.semantics_train = list() self.labels_train = list() self.masks_train = list() self.features_train = list() self.metadata_train = list() self.images_test = list() self.segmentations_test = list() self.semantics_test = list() self.labels_test = list() self.masks_test = list() self.features_test = list() self.metadata_test = list() self.Elastix_Para = list() # Set some defaults, name self.fastr_plugin = 'ProcessPoolExecution' if name == '': name = [randint(0, 9) for p in range(0, 5)] self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], 'fastr' + str(name)) else: self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], name) self.additions = dict() self.CopyMetadata = True self.segmode = []
[docs] def defaultconfig(self): """Generate a configparser object holding all default configuration values. Returns: config: configparser configuration file """ # TODO: cluster parallel execution parameters config = configparser.ConfigParser() config.optionxform = str # General configuration of WORC config['General'] = dict() config['General']['cross_validation'] = 'True' config['General']['Segmentix'] = 'False' config['General']['PCE'] = 'False' # We do not yet provide this module config['General']['FeatureCalculator'] = 'CalcFeatures' config['General']['Preprocessing'] = 'PreProcess' config['General']['RegistrationNode'] = "Elastix" config['General']['TransformationNode'] = "Transformix" config['General']['Joblib_ncores'] = '4' config['General']['Joblib_backend'] = 'multiprocessing' config['General']['tempsave'] = 'False' # Segmentix config['Segmentix'] = dict() config['Segmentix']['mask'] = 'subtract' config['Segmentix']['segtype'] = 'None' config['Segmentix']['segradius'] = '5' config['Segmentix']['N_blobs'] = '1' config['Segmentix']['fillholes'] = 'False' # Preprocessing config['Normalize'] = dict() config['Normalize']['ROI'] = 'Full' config['Normalize']['Method'] = 'z_score' # PREDICT - Feature calculation config['ImageFeatures'] = dict() config['ImageFeatures']['orientation'] = 'True' config['ImageFeatures']['texture'] = 'all' config['ImageFeatures']['coliage'] = 'False' config['ImageFeatures']['vessel'] = 'False' config['ImageFeatures']['log'] = 'False' config['ImageFeatures']['phase'] = 'False' ## Parameter settings for PREDICT feature calculation # Defines what should be done with the images config['ImageFeatures']['image_type'] = 'CT' # Define frequencies for gabor filter in pixels config['ImageFeatures']['gabor_frequencies'] = '0.05, 0.2, 0.5' # Gabor, GLCM angles in degrees and radians, respectively config['ImageFeatures']['gabor_angles'] = '0, 45, 90, 135' config['ImageFeatures']['GLCM_angles'] = '0, 0.79, 1.57, 2.36' # GLCM discretization levels, distances in pixels config['ImageFeatures']['GLCM_levels'] = '16' config['ImageFeatures']['GLCM_distances'] = '1, 3' # LBP radius, number of points in pixels config['ImageFeatures']['LBP_radius'] = '3, 8, 15' config['ImageFeatures']['LBP_npoints'] = '12, 24, 36' # Phase features minimal wavelength and number of scales config['ImageFeatures']['phase_minwavelength'] = '3' config['ImageFeatures']['phase_nscale'] = '5' # Log features sigma of Gaussian in pixels config['ImageFeatures']['log_sigma'] = '1, 5, 10' # Vessel features scale range, steps for the range config['ImageFeatures']['vessel_scale_range'] = '1, 10' config['ImageFeatures']['vessel_scale_step'] = '2' # Vessel features radius for erosion to determine boudnary config['ImageFeatures']['vessel_radius'] = '5' # Feature selection config['Featsel'] = dict() config['Featsel']['Variance'] = 'True, False' config['Featsel']['GroupwiseSearch'] = 'True' config['Featsel']['SelectFromModel'] = 'False' config['Featsel']['UsePCA'] = 'False' config['Featsel']['PCAType'] = '95variance' config['Featsel']['StatisticalTestUse'] = 'False' config['Featsel']['StatisticalTestMetric'] = 'ttest, Welch, Wilcoxon, MannWhitneyU' config['Featsel']['StatisticalTestThreshold'] = '0.02, 0.2' config['Featsel']['ReliefUse'] = 'False' config['Featsel']['ReliefNN'] = '2, 4' config['Featsel']['ReliefSampleSize'] = '1, 1' config['Featsel']['ReliefDistanceP'] = '1, 3' config['Featsel']['ReliefNumFeatures'] = '25, 200' # Groupwie Featureselection options config['SelectFeatGroup'] = dict() config['SelectFeatGroup']['shape_features'] = 'True, False' config['SelectFeatGroup']['histogram_features'] = 'True, False' config['SelectFeatGroup']['orientation_features'] = 'True, False' config['SelectFeatGroup']['texture_Gabor_features'] = 'True, False' config['SelectFeatGroup']['texture_GLCM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLCMMS_features'] = 'True, False' config['SelectFeatGroup']['texture_GLRLM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLSZM_features'] = 'True, False' config['SelectFeatGroup']['texture_NGTDM_features'] = 'True, False' config['SelectFeatGroup']['texture_LBP_features'] = 'True, False' config['SelectFeatGroup']['patient_features'] = 'False' config['SelectFeatGroup']['semantic_features'] = 'False' config['SelectFeatGroup']['coliage_features'] = 'False' config['SelectFeatGroup']['log_features'] = 'False' config['SelectFeatGroup']['vessel_features'] = 'False' config['SelectFeatGroup']['phase_features'] = 'False' # Feature imputation config['Imputation'] = dict() config['Imputation']['use'] = 'False' config['Imputation']['strategy'] = 'mean, median, most_frequent, constant, knn' config['Imputation']['n_neighbors'] = '5, 5' # Classification config['Classification'] = dict() config['Classification']['fastr'] = 'False' config['Classification']['fastr_plugin'] = self.fastr_plugin config['Classification']['classifiers'] = 'SVM' config['Classification']['max_iter'] = '100000' config['Classification']['SVMKernel'] = 'poly' config['Classification']['SVMC'] = '0, 6' config['Classification']['SVMdegree'] = '1, 6' config['Classification']['SVMcoef0'] = '0, 1' config['Classification']['SVMgamma'] = '-5, 5' config['Classification']['RFn_estimators'] = '10, 90' config['Classification']['RFmin_samples_split'] = '2, 3' config['Classification']['RFmax_depth'] = '5, 5' config['Classification']['LRpenalty'] = 'l2, l1' config['Classification']['LRC'] = '0.01, 1.0' config['Classification']['LDA_solver'] = 'svd, lsqr, eigen' config['Classification']['LDA_shrinkage'] = '-5, 5' config['Classification']['QDA_reg_param'] = '-5, 5' config['Classification']['ElasticNet_alpha'] = '-5, 5' config['Classification']['ElasticNet_l1_ratio'] = '0, 1' config['Classification']['SGD_alpha'] = '-5, 5' config['Classification']['SGD_l1_ratio'] = '0, 1' config['Classification']['SGD_loss'] = 'hinge, squared_hinge, modified_huber' config['Classification']['SGD_penalty'] = 'none, l2, l1' config['Classification']['CNB_alpha'] = '0, 1' # CrossValidation config['CrossValidation'] = dict() config['CrossValidation']['N_iterations'] = '100' config['CrossValidation']['test_size'] = '0.2' # Options for the labels that are used (not only genetics) config['Genetics'] = dict() config['Genetics']['label_names'] = 'Label1, Label2' config['Genetics']['modus'] = 'singlelabel' config['Genetics']['url'] = 'WIP' config['Genetics']['projectID'] = 'WIP' # Hyperparameter optimization options config['HyperOptimization'] = dict() config['HyperOptimization']['scoring_method'] = 'f1_weighted' config['HyperOptimization']['test_size'] = '0.15' config['HyperOptimization']['N_iterations'] = '10000' config['HyperOptimization']['n_jobspercore'] = '2000' # only relevant when using fastr in classification # Feature scaling options config['FeatureScaling'] = dict() config['FeatureScaling']['scale_features'] = 'True' config['FeatureScaling']['scaling_method'] = 'z_score' # Sample processing options config['SampleProcessing'] = dict() config['SampleProcessing']['SMOTE'] = 'True, False' config['SampleProcessing']['SMOTE_ratio'] = '1, 0' config['SampleProcessing']['SMOTE_neighbors'] = '5, 15' config['SampleProcessing']['Oversampling'] = 'False' # Ensemble options config['Ensemble'] = dict() config['Ensemble']['Use'] = 'False' # Still WIP # BUG: the FASTR XNAT plugin can only retreive folders. We therefore need to add the filenames of the resources manually # This should be fixed from fastr > 2.0.0: need to update. config['FASTR_bugs'] = dict() config['FASTR_bugs']['images'] = 'image.nii.gz' config['FASTR_bugs']['segmentations'] = 'mask.nii.gz' return config
[docs] def add_tools(self): self.Tools = Tools()
[docs] def build(self, wtype='training'): """Build the network based on the given attributes. Parameters ---------- wtype: string, default 'training' Specify the WORC execution type. - testing: use if you have a trained classifier and want to train it on some new images. - training: use if you want to train a classifier from a dataset. """ if wtype == 'training': self.build_training() self.wtype = wtype elif wtype == 'testing': self.build_testing() self.wtype = wtype
[docs] def build_training(self): """Build the training network based on the given attributes.""" # We either need images or features for Radiomics if self.images_train or self.features_train: # We currently require labels for supervised learning if self.labels_train: if not self.configs: print("No configuration given, assuming default") if self.images_train: self.configs = [self.defaultconfig()] * len(self.images_train) else: self.configs = [self.defaultconfig()] * len(self.features_train) self.network = fastr.Network('WORC_' + self.name) # BUG: We currently use the first configuration as general config image_types = list() for c in range(len(self.configs)): if type(self.configs[c]) == str: # Probably, c is a configuration file self.configs[c] = config_io.load_config(self.configs[c]) image_types.append(self.configs[c]['ImageFeatures']['image_type']) # Classification tool and label source self.network.source_patientclass_train = self.network.create_source('PatientInfoFile', id_='patientclass_train', nodegroup='pctrain') if self.labels_test: self.network.source_patientclass_test = self.network.create_source('PatientInfoFile', id_='patientclass_test', nodegroup='pctest') self.network.classify = self.network.create_node('TrainClassifier', memory='12G', id_='classify') # Outputs self.network.sink_classification = self.network.create_sink('HDF5', id_='classification') self.network.sink_performance = self.network.create_sink('JsonFile', id_='performance') # Links self.network.source_class_config = self.network.create_source('ParameterFile', id_='config_classification', nodegroup='conf') # self.network.source_class_parameters = self.network.create_source('JsonFile', id_='parameters_classification') # self.network.classify.inputs['parameters'] = self.network.source_class_parameters.output self.network.link_class_1 = self.network.create_link(self.network.source_class_config.output, self.network.classify.inputs['config'][0]) self.network.link_class_2 = self.network.create_link(self.network.source_patientclass_train.output, self.network.classify.inputs['patientclass_train']) self.network.link_class_1.collapse = 'conf' self.network.link_class_2.collapse = 'pctrain' if self.images_test or self.features_test: # FIXME: the naming here is ugly self.network.link_class_3 = self.network.create_link(self.network.source_patientclass_test.output, self.network.classify.inputs['patientclass_test']) self.network.link_class_3.collapse = 'pctest' self.network.sink_classification.input = self.network.classify.outputs['classification'] self.network.sink_performance.input = self.network.classify.outputs['performance'] if not self.features_train: # Create nodes to compute features self.network.sources_parameters = dict() self.network.calcfeatures_train = dict() self.network.preprocessing_train = dict() self.network.sources_images_train = dict() self.network.sinks_features_train = dict() self.network.converters_im_train = dict() self.network.converters_seg_train = dict() self.network.links_C1_train = dict() if self.images_test or self.features_test: # A test set is supplied, for which nodes also need to be created self.network.preprocessing_test = dict() self.network.calcfeatures_test = dict() self.network.sources_images_test = dict() self.network.sinks_features_test = dict() self.network.converters_im_test = dict() self.network.converters_seg_test = dict() self.network.links_C1_test = dict() # Check which nodes are necessary if not self.segmentations_train: message = "No automatic segmentation method is yet implemented." raise WORCexceptions.WORCNotImplementedError(message) elif len(self.segmentations_train) == len(image_types): # Segmentations provided self.network.sources_segmentations_train = dict() self.network.sources_segmentations_test = dict() self.segmode = 'Provided' elif len(self.segmentations_train) == 1: # Assume segmentations need to be registered to other modalities self.network.sources_segmentation = dict() self.segmode = 'Register' self.network.source_Elastix_Parameters = dict() self.network.elastix_nodes_train = dict() self.network.transformix_seg_nodes_train = dict() self.network.sources_segmentations_train = dict() self.network.sinks_transformations_train = dict() self.network.sinks_segmentations_elastix_train = dict() self.network.sinks_images_elastix_train = dict() self.network.converters_seg_train = dict() self.network.edittransformfile_nodes_train = dict() self.network.transformix_im_nodes_train = dict() self.network.elastix_nodes_test = dict() self.network.transformix_seg_nodes_test = dict() self.network.sources_segmentations_test = dict() self.network.sinks_transformations_test = dict() self.network.sinks_segmentations_elastix_test = dict() self.network.sinks_images_elastix_test = dict() self.network.converters_seg_test = dict() self.network.edittransformfile_nodes_test = dict() self.network.transformix_im_nodes_test = dict() pass # BUG: We assume that first type defines if we use segmentix if self.configs[0]['General']['Segmentix'] == 'True': # Use the segmentix toolbox for segmentation processing self.network.sinks_segmentations_segmentix_train = dict() self.network.sources_masks_train = dict() self.network.converters_masks_train = dict() self.network.nodes_segmentix_train = dict() if self.images_test or self.features_test: # Also use segmentix on the tes set self.network.sinks_segmentations_segmentix_test = dict() self.network.sources_masks_test = dict() self.network.converters_masks_test = dict() self.network.nodes_segmentix_test = dict() if self.semantics_train: # Semantic features are supplied self.network.sources_semantics_train = dict() if self.metadata_train: # Metadata to extract patient features from is supplied self.network.sources_metadata_train = dict() if self.semantics_test: # Semantic features are supplied self.network.sources_semantics_test = dict() if self.metadata_test: # Metadata to extract patient features from is supplied self.network.sources_metadata_test = dict() # Create a part of the pipeline for each modality self.modlabels = list() for nmod, mod in enumerate(image_types): # Create label for each modality/image num = 0 label = mod + '_' + str(num) while label in self.network.calcfeatures_train.keys(): # if label already exists, add number to label num += 1 label = mod + '_' + str(num) self.modlabels.append(label) # Create required sources and sinks self.network.sources_parameters[label] = self.network.create_source('ParameterFile', id_='parameters_' + label) self.network.sources_images_train[label] = self.network.create_source('ITKImageFile', id_='images_train_' + label, nodegroup='train') self.network.sinks_features_train[label] = self.network.create_sink('HDF5', id_='features_train_' + label) if self.images_test or self.features_test: self.network.sources_images_test[label] = self.network.create_source('ITKImageFile', id_='images_test_' + label, nodegroup='test') self.network.sinks_features_test[label] = self.network.create_sink('HDF5', id_='features_test_' + label) if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.network.sources_metadata_train[label] = self.network.create_source('DicomImageFile', id_='metadata_train_' + label, nodegroup='train') if self.metadata_test and len(self.metadata_test) >= nmod + 1: self.network.sources_metadata_test[label] = self.network.create_source('DicomImageFile', id_='metadata_test_' + label, nodegroup='test') if self.masks_train and len(self.masks_train) >= nmod + 1: # Create mask source and convert self.network.sources_masks_train[label] = self.network.create_source('ITKImageFile', id_='mask_train_' + label, nodegroup='train') self.network.converters_masks_train[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_mask_train_' + label, nodegroup='train') self.network.converters_masks_train[label].inputs['image'] = self.network.sources_masks_train[label].output if self.masks_test and len(self.masks_test) >= nmod + 1: # Create mask source and convert self.network.sources_masks_test[label] = self.network.create_source('ITKImageFile', id_='mask_test_' + label, nodegroup='test') self.network.converters_masks_test[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_mask_test_' + label, nodegroup='test') self.network.converters_masks_test[label].inputs['image'] = self.network.sources_masks_test[label].output # First convert the images if any(modality in mod for modality in ['MR', 'CT', 'MG', 'PET']): # Use ITKTools PXCastConvet for converting image formats self.network.converters_im_train[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_im_train_' + label) if self.images_test or self.features_test: self.network.converters_im_test[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_im_test_' + label) elif 'DTI' in mod: # TODO: This reader is currently missing self.network.converters_im_train[label] = self.network.create_node('DTIreader', memory='4G', id_='convert_im_train_' + label) if self.images_test or self.features_test: self.network.converters_im_test[label] = self.network.create_node('DTIreader', memory='4G', id_='convert_im_test_' + label) else: raise WORCexceptions.WORCTypeError(('No valid image type for modality {}: {} provided.').format(str(nmod), mod)) # Create required links self.network.converters_im_train[label].inputs['image'] = self.network.sources_images_train[label].output if self.images_test or self.features_test: self.network.converters_im_test[label].inputs['image'] = self.network.sources_images_test[label].output # ----------------------------------------------------- # Preprocessing # Create nodes preprocess_node = str(self.configs[nmod]['General']['Preprocessing']) self.network.preprocessing_train[label] = self.network.create_node(preprocess_node, memory='4G', id_='preprocessing_train_' + label) if self.images_test or self.features_test: self.network.preprocessing_test[label] = self.network.create_node(preprocess_node, memory='4G', id_='preprocessing_test_' + label) # Create required links self.network.preprocessing_train[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.preprocessing_train[label].inputs['image'] = self.network.converters_im_train[label].outputs['image'] if self.images_test or self.features_test: self.network.preprocessing_test[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.preprocessing_test[label].inputs['image'] = self.network.converters_im_test[label].outputs['image'] if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.network.preprocessing_train[label].inputs['metadata'] = self.network.sources_metadata_train[label].output if self.metadata_test and len(self.metadata_test) >= nmod + 1: self.network.preprocessing_test[label].inputs['metadata'] = self.network.sources_metadata_test[label].output # ----------------------------------------------------- # Create a feature calculator node calcfeat_node = str(self.configs[nmod]['General']['FeatureCalculator']) self.network.calcfeatures_train[label] = self.network.create_node(calcfeat_node, memory='14G', id_='calcfeatures_train_' + label) if self.images_test or self.features_test: self.network.calcfeatures_test[label] = self.network.create_node(calcfeat_node, memory='14G', id_='calcfeatures_test_' + label) # Create required links self.network.calcfeatures_train[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.calcfeatures_train[label].inputs['image'] = self.network.preprocessing_train[label].outputs['image'] if self.images_test or self.features_test: self.network.calcfeatures_test[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.calcfeatures_test[label].inputs['image'] = self.network.preprocessing_test[label].outputs['image'] if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.network.calcfeatures_train[label].inputs['metadata'] = self.network.sources_metadata_train[label].output if self.metadata_train and len(self.metadata_test) >= nmod + 1: self.network.calcfeatures_train[label].inputs['metadata'] = self.network.sources_metadata_train[label].output if self.semantics_train and len(self.semantics_train) >= nmod + 1: self.network.sources_semantics_train[label] = self.network.create_source('CSVFile', id_='semantics_train_' + label) self.network.calcfeatures_train[label].inputs['semantics'] = self.network.sources_semantics_train[label].output if self.semantics_test and len(self.semantics_test) >= nmod + 1: self.network.sources_semantics_test[label] = self.network.create_source('CSVFile', id_='semantics_test_' + label) self.network.calcfeatures_test[label].inputs['semantics'] = self.network.sources_semantics_test[label].output if self.segmode == 'Provided': # Segmentation ----------------------------------------------------- # Use the provided segmantions for each modality self.network.sources_segmentations_train[label] = self.network.create_source('ITKImageFile', id_='segmentations_train_' + label, nodegroup='train') self.network.converters_seg_train[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_seg_train_' + label) self.network.converters_seg_train[label].inputs['image'] = self.network.sources_segmentations_train[label].output if self.images_test or self.features_test: self.network.sources_segmentations_test[label] = self.network.create_source('ITKImageFile', id_='segmentations_test_' + label, nodegroup='test') self.network.converters_seg_test[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_seg_test_' + label) self.network.converters_seg_test[label].inputs['image'] = self.network.sources_segmentations_test[label].output elif self.segmode == 'Register': # Registration nodes ----------------------------------------------------- # Align segmentation of first modality to others using registration with Elastix # Create sources and converter for only for the given segmentation, which should be on the first modality if nmod == 0: self.network.sources_segmentations_train[label] = self.network.create_source('ITKImageFile', id_='segmentations_train_' + label, nodegroup='input') self.network.converters_seg_train[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_seg_train_' + label) self.network.converters_seg_train[label].inputs['image'] = self.network.sources_segmentations_train[label].output if self.images_test or self.features_test: self.network.sources_segmentations_test[label] = self.network.create_source('ITKImageFile', id_='segmentations_test_' + label, nodegroup='input') self.network.converters_seg_test[label] = self.network.create_node('WORCCastConvert', memory='4G', id_='convert_seg_test_' + label) self.network.converters_seg_test[label].inputs['image'] = self.network.sources_segmentations_test[label].output # Assume provided segmentation is on first modality if nmod > 0: # Use elastix and transformix for registration # NOTE: Assume elastix node type is on first configuration elastix_node = str(self.configs[0]['General']['RegistrationNode']) transformix_node = str(self.configs[0]['General']['TransformationNode']) self.network.elastix_nodes_train[label] = self.network.create_node(elastix_node, memory='4G', id_='elastix_train_' + label) self.network.transformix_seg_nodes_train[label] = self.network.create_node(transformix_node, id_='transformix_seg_train_' + label) self.network.transformix_im_nodes_train[label] = self.network.create_node(transformix_node, id_='transformix_im_train_' + label) if self.images_test or self.features_test: self.network.elastix_nodes_test[label] = self.network.create_node(elastix_node, memory='4G', id_='elastix_test_' + label) self.network.transformix_seg_nodes_test[label] = self.network.create_node(transformix_node, id_='transformix_seg_test_' + label) self.network.transformix_im_nodes_test[label] = self.network.create_node(transformix_node, id_='transformix_im_test_' + label) # Create sources_segmentation # M1 = moving, others = fixed self.network.elastix_nodes_train[label].inputs['fixed_image'] = self.network.converters_im_train[label].outputs['image'] self.network.elastix_nodes_train[label].inputs['moving_image'] = self.network.converters_im_train[self.modlabels[0]].outputs['image'] # Add node that copies metadata from the image to the segmentation if required if self.CopyMetadata: # Copy metadata from the image which was registered to the segmentation, if it is not created yet if not hasattr(self.network, "copymetadata_nodes_train"): # NOTE: Do this for first modality, as we assume segmentation is on that one self.network.copymetadata_nodes_train = dict() self.network.copymetadata_nodes_train[self.modlabels[0]] = self.network.create_node("CopyMetadata", id_='CopyMetadata_train_' + self.modlabels[0]) self.network.copymetadata_nodes_train[self.modlabels[0]].inputs["source"] = self.network.converters_im_train[self.modlabels[0]].outputs['image'] self.network.copymetadata_nodes_train[self.modlabels[0]].inputs["destination"] = self.network.converters_seg_train[self.modlabels[0]].outputs['image'] self.network.transformix_seg_nodes_train[label].inputs['image'] = self.network.copymetadata_nodes_train[self.modlabels[0]].outputs['output'] else: self.network.transformix_seg_nodes_train[label].inputs['image'] = self.network.converters_seg_train[self.modlabels[0]].outputs['image'] if self.images_test or self.features_test: self.network.elastix_nodes_test[label].inputs['fixed_image'] = self.network.converters_im_test[label].outputs['image'] self.network.elastix_nodes_test[label].inputs['moving_image'] = self.network.converters_im_test[self.modlabels[0]].outputs['image'] if self.CopyMetadata: # Copy metadata from the image which was registered to the segmentation if not hasattr(self.network, "copymetadata_nodes_test"): # NOTE: Do this for first modality, as we assume segmentation is on that one self.network.copymetadata_nodes_test = dict() self.network.copymetadata_nodes_test[self.modlabels[0]] = self.network.create_node("CopyMetadata", id_='CopyMetadata_test_' + self.modlabels[0]) self.network.copymetadata_nodes_test[self.modlabels[0]].inputs["source"] = self.network.converters_im_test[self.modlabels[0]].outputs['image'] self.network.copymetadata_nodes_test[self.modlabels[0]].inputs["destination"] = self.network.converters_seg_test[self.modlabels[0]].outputs['image'] self.network.transformix_seg_nodes_test[label].inputs['image'] = self.network.copymetadata_nodes_test[self.modlabels[0]].outputs['output'] else: self.network.transformix_seg_nodes_test[label].inputs['image'] = self.network.converters_seg_test[self.modlabels[0]].outputs['image'] # Apply registration to input modalities self.network.source_Elastix_Parameters[label] = self.network.create_source('ElastixParameterFile', id_='Elastix_Para_' + label, nodegroup='elpara') self.link_elparam_train = self.network.create_link(self.network.source_Elastix_Parameters[label].output, self.network.elastix_nodes_train[label].inputs['parameters']) self.link_elparam_train.collapse = 'elpara' if self.images_test or self.features_test: self.link_elparam_test = self.network.create_link(self.network.source_Elastix_Parameters[label].output, self.network.elastix_nodes_test[label].inputs['parameters']) self.link_elparam_test.collapse = 'elpara' if self.masks_train: self.network.elastix_nodes_train[label].inputs['fixed_mask'] = self.network.converters_masks_train[label].outputs['image'] self.network.elastix_nodes_train[label].inputs['moving_mask'] = self.network.converters_masks_train[self.modlabels[0]].outputs['image'] if self.images_test or self.features_test: if self.masks_test: self.network.elastix_nodes_test[label].inputs['fixed_mask'] = self.network.converters_masks_test[label].outputs['image'] self.network.elastix_nodes_test[label].inputs['moving_mask'] = self.network.converters_masks_test[self.modlabels[0]].outputs['image'] # Change the FinalBSpline Interpolation order to 0 as required for binarie images: see https://github.com/SuperElastix/elastix/wiki/FAQ self.network.edittransformfile_nodes_train[label] = self.network.create_node('EditElastixTransformFile', id_='EditElastixTransformFile' + label) self.network.edittransformfile_nodes_train[label].inputs['set'] = ["FinalBSplineInterpolationOrder=0"] self.network.edittransformfile_nodes_train[label].inputs['transform'] = self.network.elastix_nodes_train[label].outputs['transform'][-1] if self.images_test or self.features_test: self.network.edittransformfile_nodes_test[label] = self.network.create_node('EditElastixTransformFile', id_='EditElastixTransformFile' + label) self.network.edittransformfile_nodes_test[label].inputs['set'] = ["FinalBSplineInterpolationOrder=0"] self.network.edittransformfile_nodes_test[label].inputs['transform'] = self.network.elastix_nodes_test[label].outputs['transform'][-1] # Link data and transformation to transformix and source self.network.transformix_seg_nodes_train[label].inputs['transform'] = self.network.edittransformfile_nodes_train[label].outputs['transform'] self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.transformix_seg_nodes_train[label].outputs['image'] self.network.transformix_im_nodes_train[label].inputs['transform'] = self.network.elastix_nodes_train[label].outputs['transform'][-1] self.network.transformix_im_nodes_train[label].inputs['image'] = self.network.converters_im_train[self.modlabels[0]].outputs['image'] if self.images_test or self.features_test: self.network.transformix_seg_nodes_test[label].inputs['transform'] = self.network.edittransformfile_nodes_test[label].outputs['transform'] self.network.calcfeatures_test[label].inputs['segmentation'] = self.network.transformix_seg_nodes_test[label] .outputs['image'] self.network.transformix_im_nodes_test[label].inputs['transform'] = self.network.elastix_nodes_test[label].outputs['transform'][-1] self.network.transformix_im_nodes_test[label].inputs['image'] = self.network.converters_im_test[self.modlabels[0]].outputs['image'] # Save output self.network.sinks_transformations_train[label] = self.network.create_sink('ElastixTransformFile', id_='transformations_train_' + label) self.network.sinks_segmentations_elastix_train[label] = self.network.create_sink('ITKImageFile', id_='segmentations_out_elastix_train_' + label) self.network.sinks_images_elastix_train[label] = self.network.create_sink('ITKImageFile', id_='images_out_elastix_train_' + label) self.network.sinks_transformations_train[label].input = self.network.elastix_nodes_train[label].outputs['transform'] self.network.sinks_segmentations_elastix_train[label].input = self.network.transformix_seg_nodes_train[label].outputs['image'] self.network.sinks_images_elastix_train[label].input = self.network.transformix_im_nodes_train[label].outputs['image'] if self.images_test or self.features_test: self.network.sinks_transformations_test[label] = self.network.create_sink('ElastixTransformFile', id_='transformations_test_' + label) self.network.sinks_segmentations_elastix_test[label] = self.network.create_sink('ITKImageFile', id_='segmentations_out_elastix_test_' + label) self.network.sinks_images_elastix_test[label] = self.network.create_sink('ITKImageFile', id_='images_out_elastix_test_' + label) self.network.sinks_transformations_elastix_test[label].input = self.network.elastix_nodes_test[label].outputs['transform'] self.network.sinks_segmentations_elastix_test[label].input = self.network.transformix_seg_nodes_test[label].outputs['image'] self.network.sinks_images_elastix_test[label].input = self.network.transformix_im_nodes_test[label].outputs['image'] if self.configs[nmod]['General']['Segmentix'] == 'True': # Segmentix nodes ----------------------------------------------------- # Use segmentix node to convert input segmentation into correct contour if label not in self.network.sinks_segmentations_segmentix_train: self.network.sinks_segmentations_segmentix_train[label] = self.network.create_sink('ITKImageFile', id_='segmentations_out_segmentix_train_' + label) self.network.nodes_segmentix_train[label] = self.network.create_node('Segmentix', memory='6G', id_='segmentix_train_' + label) if hasattr(self.network, 'transformix_seg_nodes_train'): if label in self.network.transformix_seg_nodes_train.keys(): # Use output of registration in segmentix self.network.nodes_segmentix_train[label].inputs['segmentation_in'] = self.network.transformix_seg_nodes_train[label].outputs['image'] else: # Use original segmentation self.network.nodes_segmentix_train[label].inputs['segmentation_in'] = self.network.converters_seg_train[label].outputs['image'] else: # Use original segmentation self.network.nodes_segmentix_train[label].inputs['segmentation_in'] = self.network.converters_seg_train[label].outputs['image'] self.network.nodes_segmentix_train[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.nodes_segmentix_train[label].outputs['segmentation_out'] self.network.sinks_segmentations_segmentix_train[label].input = self.network.nodes_segmentix_train[label].outputs['segmentation_out'] if self.images_test or self.features_test: self.network.sinks_segmentations_segmentix_test[label] = self.network.create_sink('ITKImageFile', id_='segmentations_out_segmentix_test_' + label) self.network.nodes_segmentix_test[label] = self.network.create_node('Segmentix', memory='6G', id_='segmentix_test_' + label) if hasattr(self.network, 'transformix_seg_nodes_test'): if label in self.network.transformix_seg_nodes_test.keys(): # Use output of registration in segmentix self.network.nodes_segmentix_test[label].inputs['segmentation_in'] = self.network.transformix_seg_nodes_test[label].outputs['image'] else: # Use original segmentation self.network.nodes_segmentix_test[label].inputs['segmentation_in'] = self.network.converters_seg_test[label].outputs['image'] else: # Use original segmentation self.network.nodes_segmentix_test[label].inputs['segmentation_in'] = self.network.converters_seg_test[label].outputs['image'] self.network.nodes_segmentix_test[label].inputs['parameters'] = self.network.sources_parameters[label].output self.network.calcfeatures_test[label].inputs['segmentation'] = self.network.nodes_segmentix_test[label].outputs['segmentation_out'] self.network.sinks_segmentations_segmentix_test[label].input = self.network.nodes_segmentix_test[label].outputs['segmentation_out'] if self.masks_train: # Use masks self.network.nodes_segmentix_train[label].inputs['mask'] = self.network.converters_masks_train[label].outputs['image'] if self.masks_test: # Use masks self.network.nodes_segmentix_test[label].inputs['mask'] = self.network.converters_masks_test[label].outputs['image'] else: if self.segmode == 'Provided': self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.converters_seg_train[label].outputs['image'] elif self.segmode == 'Register': if nmod > 0: self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.transformix_seg_nodes_train[label].outputs['image'] else: self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.converters_seg_train[label].outputs['image'] if self.images_test or self.features_test: if self.segmode == 'Provided': self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.converters_seg_train[label].outputs['image'] elif self.segmode == 'Register': if nmod > 0: self.network.calcfeatures_test[label].inputs['segmentation'] = self.network.transformix_seg_nodes_test[label] .outputs['image'] else: self.network.calcfeatures_train[label].inputs['segmentation'] = self.network.converters_seg_train[label].outputs['image'] # Classification nodes ----------------------------------------------------- # Add the features from this modality to the classifier node input self.network.links_C1_train[label] = self.network.classify.inputs['features_train'][str(label)] << self.network.calcfeatures_train[label].outputs['features'] self.network.links_C1_train[label].collapse = 'train' if self.images_test or self.features_test: # Add the features from this modality to the classifier node input self.network.links_C1_test[label] = self.network.classify.inputs['features_test'][str(label)] << self.network.calcfeatures_test[label].outputs['features'] self.network.links_C1_test[label].collapse = 'test' # Save output self.network.sinks_features_train[label].input = self.network.calcfeatures_train[label].outputs['features'] if self.images_test or self.features_test: self.network.sinks_features_test[label].input = self.network.calcfeatures_test[label].outputs['features'] else: # Features already provided: hence we can skip numerous nodes self.network.sources_features_train = dict() self.network.links_C1_train = dict() if self.features_test: self.network.sources_features_test = dict() self.network.links_C1_test = dict() # Create label for each modality/image self.modlabels = list() for num, mod in enumerate(image_types): num = 0 label = mod + str(num) while label in self.network.sources_features_train.keys(): # if label exists, add number to label num += 1 label = mod + str(num) self.modlabels.append(label) # Create a node for the feature computation self.network.sources_features_train[label] = self.network.create_source('HDF5', id_='features_train_' + label, nodegroup='train') # Add the features from this modality to the classifier node input self.network.links_C1_train[label] = self.network.classify.inputs['features_train'][str(label)] << self.network.sources_features_train[label].output self.network.links_C1_train[label].collapse = 'train' if self.features_test: self.network.sources_features_test[label] = self.network.create_source('HDF5', id_='features_test_' + label, nodegroup='test') self.network.links_C1_test[label] = self.network.classify.inputs['features_test'][str(label)] << self.network.sources_features_test[label].output self.network.links_C1_test[label].collapse = 'test' if self.configs[num]['General']['PCE'] == 'True': # NOTE: PCE feature is currently not open-source. self.network.PCEnode = self.network.create_node('PCE', memory='64G', id_='PCE') self.network.PCEnode.inputs['svm'] = self.network.classify.outputs['classification'] self.network.sink_PCE = self.network.create_sink('MatlabFile', id_='PCE_mat') self.network.sink_SI = self.network.create_sink('MatlabFile', id_='SI_mat') self.network.sink_TS = self.network.create_sink('CSVFile', id_='TS_csv') self.network.sink_PCE.input = self.network.PCEnode.outputs['PCE'] self.network.sink_SI.input = self.network.PCEnode.outputs['SI'] self.network.sink_TS.input = self.network.PCEnode.outputs['TS'] else: raise WORCexceptions.WORCIOError("Please provide labels.") else: raise WORCexceptions.WORCIOError("Please provide either images or features.")
[docs] def build_testing(self): ''' todo '''
[docs] def set(self): """ Set the FASTR source and sink data based on the given attributes.""" self.fastrconfigs = list() self.source_data = dict() self.sink_data = dict() # If the configuration files are confiparse objects, write to file for num, c in enumerate(self.configs): if type(c) != configparser.ConfigParser: # A filepath (not a fastr source) is provided. Hence we read # the config file and convert it to a configparser object config = configparser.ConfigParser() config.read(c) c = config cfile = os.path.join(fastr.config.mounts['tmp'], self.name, ("config_{}_{}.ini").format(self.name, num)) if not os.path.exists(os.path.dirname(cfile)): os.makedirs(os.path.dirname(cfile)) with open(cfile, 'w') as configfile: c.write(configfile) self.fastrconfigs.append(os.path.join("vfs://tmp", self.name, ("config_{}_{}.ini").format(self.name, num))) # Generate gridsearch parameter files if required # TODO: We now use the first configuration for the classifier, but his needs to be separated from the rest per modality self.source_data['config_classification'] = self.fastrconfigs[0] # Set source and sink data self.source_data['patientclass_train'] = self.labels_train self.source_data['patientclass_test'] = self.labels_test self.sink_data['classification'] = ("vfs://output/{}/svm_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['performance'] = ("vfs://output/{}/performance_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) if self.configs[0]['General']['PCE']: self.sink_data['PCE_mat'] = ("vfs://output/{}/PCE_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['SI_mat'] = ("vfs://output/{}/SI_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['TS_csv'] = ("vfs://output/{}/TS_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) # NOTE: Below bug should be fixed, need to check # BUG: this is a bug in the FASTR package. Workaround for nifti XNAT links using expansion of FASTR XNAT plugin. for num, im in enumerate(self.images_train): if im is not None and 'xnat' in im: if 'DICOM' not in im: fastr.ioplugins['xnat'] # metalink = im_m1.replace('NIFTI','DICOM') im = fastr.ioplugins['xnat'].expand_url(im) imagename = self.configs[num]['FASTR_bugs']['images'] self.images_train[num] = {v[(v.find('subjects') + 9):v.find('/experiments')]: v + imagename + '?insecure=true' for k, v in im} for num, seg in enumerate(self.segmentations_train): if seg is not None and 'xnat' in seg: fastr.ioplugins['xnat'] seg = fastr.ioplugins['xnat'].expand_url(seg) segname = self.configs[num]['FASTR_bugs']['segmentations'] self.segmentations_train[num] = {v[(v.find('subjects') + 9):v.find('/experiments')]: v + segname + '?insecure=true' for k, v in seg} # Set the source data from the WORC objects you created for num, label in enumerate(self.modlabels): self.source_data['parameters_' + label] = self.fastrconfigs[num] # Add train data sources if self.images_train and len(self.images_train) - 1 >= num: self.source_data['images_train_' + label] = self.images_train[num] if self.masks_train and len(self.masks_train) - 1 >= num: self.source_data['mask_train_' + label] = self.masks_train[num] if self.metadata_train and len(self.metadata_train) - 1 >= num: self.source_data['metadata_train_' + label] = self.metadata_train[num] if self.segmentations_train and len(self.segmentations_train) - 1 >= num: self.source_data['segmentations_train_' + label] = self.segmentations_train[num] if self.semantics_train and len(self.semantics_train) - 1 >= num: self.source_data['semantics_train_' + label] = self.semantics_train[num] if self.features_train and len(self.features_train) - 1 >= num: self.source_data['features_train_' + label] = self.features_train[num] if self.Elastix_Para: # First modality does not need to be registered if num > 0: if len(self.Elastix_Para) > 1: # Each modality has its own registration parameters self.source_data['Elastix_Para_' + label] = self.Elastix_Para[num] else: # Use one fileset for all modalities self.source_data['Elastix_Para_' + label] = self.Elastix_Para[0] # Add test data sources if self.images_test and len(self.images_test) - 1 >= num: self.source_data['images_test_' + label] = self.images_test[num] if self.masks_test and len(self.masks_test) - 1 >= num: self.source_data['mask_test_' + label] = self.masks_test[num] if self.metadata_test and len(self.metadata_test) - 1 >= num: self.source_data['metadata_test_' + label] = self.metadata_test[num] if self.segmentations_test and len(self.segmentations_test) - 1 >= num: self.source_data['segmentations_test_' + label] = self.segmentations_test[num] if self.semantics_test and len(self.semantics_test) - 1 >= num: self.source_data['semantics_test_' + label] = self.semantics_test[num] if self.features_test and len(self.features_test) - 1 >= num: self.source_data['features_test_' + label] = self.features_test[num] self.sink_data['segmentations_out_segmentix_train_' + label] = ("vfs://output/{}/Segmentations/seg_{}_segmentix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['segmentations_out_elastix_train_' + label] = ("vfs://output/{}/Elastix/seg_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['images_out_elastix_train_' + label] = ("vfs://output/{}/Elastix/im_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['features_train_' + label] = ("vfs://output/{}/Features/features_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if self.labels_test: self.sink_data['segmentations_out_segmentix_test_' + label] = ("vfs://output/Segmentations/{}/seg_{}_segmentix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['segmentations_out_elastix_test_' + label] = ("vfs://output/{}/Elastix/seg_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['images_out_elastix_test_' + label] = ("vfs://output/{}/Images/im_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['features_test_' + label] = ("vfs://output/{}/Features/features_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) # Add elastix sinks if used if self.segmode: # Segmode is only non-empty if segmentations are provided if self.segmode == 'Register': self.sink_data['transformations_train_' + label] = ("vfs://output/{}/Elastix/transformation_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if self.images_test or self.features_test: self.sink_data['transformations_test_' + label] = ("vfs://output/{}/Elastix/transformation_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label)
[docs] def execute(self): """ Execute the network through the fastr.network.execute command. """ # Draw and execute nwtwork self.network.draw_network(self.network.id, draw_dimension=True) self.network.execute(self.source_data, self.sink_data, execution_plugin=self.fastr_plugin, tmpdir=self.fastr_tmpdir)
[docs]class Tools(object): ''' This object can be used to create other pipelines besides the default Radiomics executions. Currently only includes a registratio pipeline. '''
[docs] def __init__(self): self.Elastix = Elastix() self.Evaluate = Evaluate()