def convert( picklable_brain_image: PicklableBrainImage ) -> structure.BrainImage: """Converts a :class:`PicklableBrainImage` to :class:`BrainImage <data.structure.BrainImage>`. Args: picklable_brain_image (PicklableBrainImage): A pickable brain image. Returns: BrainImage: The brain image. """ images = {} for key, np_img in picklable_brain_image.np_images.items(): images[key] = conversion.NumpySimpleITKImageBridge.convert( np_img, picklable_brain_image.image_properties) feature_images = {} for key, np_feat_img in picklable_brain_image.np_feature_images.items( ): feature_images[key] = conversion.NumpySimpleITKImageBridge.convert( np_feat_img, picklable_brain_image.image_properties) brain_image = structure.BrainImage(picklable_brain_image.id_, picklable_brain_image.path, images) brain_image.feature_matrix = picklable_brain_image.feature_matrix return brain_image
def pre_process(id_: str, paths: dict, **kwargs) -> structure.BrainImage: """Loads and processes an image. The processing includes: - Registration - Pre-processing - Feature extraction Args: id_ (str): An image identifier. paths (dict): A dict, where the keys are an image identifier of type structure.BrainImageTypes and the values are paths to the images. Returns: (structure.BrainImage): """ print('-' * 10, 'Processing', id_) # load image path = paths.pop( id_, '') # the value with key id_ is the root directory of the image img = {img_key: sitk.ReadImage(path) for img_key, path in paths.items()} img = structure.BrainImage(id_, path, img) # construct T1 pipeline pipeline_t1 = fltr.FilterPipeline() if kwargs.get('zscore_pre', False): pipeline_t1.add_filter(fltr_prep.NormalizeZScore()) if kwargs.get('registration_pre', False): pipeline_t1.add_filter(fltr_reg.MultiModalRegistration()) pipeline_t1.set_param(fltr_reg.MultiModalRegistrationParams(atlas_t1), 1) # execute pipeline on T1 image img.images[structure.BrainImageTypes.T1] = pipeline_t1.execute( img.images[structure.BrainImageTypes.T1]) # construct T2 pipeline pipeline_t2 = fltr.FilterPipeline() if kwargs.get('zscore_pre', False): pipeline_t2.add_filter(fltr_prep.NormalizeZScore()) # execute pipeline on T2 image img.images[structure.BrainImageTypes.T2] = pipeline_t2.execute( img.images[structure.BrainImageTypes.T2]) if kwargs.get('registration_pre', False): # get transformation transform = pipeline_t1.filters[1].transform # apply transformation of T1 image registration to T2 image image_t2 = img.images[structure.BrainImageTypes.T2] image_t2 = sitk.Resample(image_t2, atlas_t1, transform, sitk.sitkLinear, 0.0, image_t2.GetPixelIDValue()) img.images[structure.BrainImageTypes.T2] = image_t2 # apply transformation of T1 image registration to ground truth image_ground_truth = img.images[structure.BrainImageTypes.GroundTruth] image_ground_truth = sitk.Resample( image_ground_truth, atlas_t1, transform, sitk.sitkNearestNeighbor, 0, image_ground_truth.GetPixelIDValue()) img.images[structure.BrainImageTypes.GroundTruth] = image_ground_truth # update image properties to atlas image properties after registration img.image_properties = conversion.ImageProperties(atlas_t1) # extract the features feature_extractor = FeatureExtractor(img, **kwargs) img = feature_extractor.execute() img.feature_images = {} return img
def pre_process(id_: str, paths: dict, **kwargs) -> structure.BrainImage: """Loads and processes an image. The processing includes: - Registration - Pre-processing - Feature extraction Args: id_ (str): An image identifier. paths (dict): A dict, where the keys are an image identifier of type structure.BrainImageTypes and the values are paths to the images. Returns: (structure.BrainImage): """ print('-' * 10, 'Processing', id_) # load image path = paths.pop( id_, '') # the value with key id_ is the root directory of the image path_to_transform = paths.pop( structure.BrainImageTypes.RegistrationTransform, '') img = {img_key: sitk.ReadImage(path) for img_key, path in paths.items()} transform = sitk.ReadTransform(path_to_transform) img = structure.BrainImage(id_, path, img, transform) # construct pipeline for brain mask registration # we need to perform this before the T1w and T2w pipeline because the registered mask is used for skull-stripping pipeline_brain_mask = fltr.FilterPipeline() if kwargs.get('registration_pre', False): pipeline_brain_mask.add_filter(fltr_prep.ImageRegistration()) pipeline_brain_mask.set_param( fltr_prep.ImageRegistrationParameters(atlas_t1, img.transformation, True), len(pipeline_brain_mask.filters) - 1) # execute pipeline on the brain mask image img.images[ structure.BrainImageTypes.BrainMask] = pipeline_brain_mask.execute( img.images[structure.BrainImageTypes.BrainMask]) # construct pipeline for T1w image pre-processing pipeline_t1 = fltr.FilterPipeline() if kwargs.get('registration_pre', False): pipeline_t1.add_filter(fltr_prep.ImageRegistration()) pipeline_t1.set_param( fltr_prep.ImageRegistrationParameters(atlas_t1, img.transformation), len(pipeline_t1.filters) - 1) if kwargs.get('skullstrip_pre', False): pipeline_t1.add_filter(fltr_prep.SkullStripping()) pipeline_t1.set_param( fltr_prep.SkullStrippingParameters( img.images[structure.BrainImageTypes.BrainMask]), len(pipeline_t1.filters) - 1) if kwargs.get('normalization_pre', False): pipeline_t1.add_filter(fltr_prep.ImageNormalization()) # execute pipeline on the T1w image img.images[structure.BrainImageTypes.T1w] = pipeline_t1.execute( img.images[structure.BrainImageTypes.T1w]) # construct pipeline for T2w image pre-processing pipeline_t2 = fltr.FilterPipeline() if kwargs.get('registration_pre', False): pipeline_t2.add_filter(fltr_prep.ImageRegistration()) pipeline_t2.set_param( fltr_prep.ImageRegistrationParameters(atlas_t2, img.transformation), len(pipeline_t2.filters) - 1) if kwargs.get('skullstrip_pre', False): pipeline_t2.add_filter(fltr_prep.SkullStripping()) pipeline_t2.set_param( fltr_prep.SkullStrippingParameters( img.images[structure.BrainImageTypes.BrainMask]), len(pipeline_t2.filters) - 1) if kwargs.get('normalization_pre', False): pipeline_t2.add_filter(fltr_prep.ImageNormalization()) # execute pipeline on the T2w image img.images[structure.BrainImageTypes.T2w] = pipeline_t2.execute( img.images[structure.BrainImageTypes.T2w]) # construct pipeline for ground truth image pre-processing pipeline_gt = fltr.FilterPipeline() if kwargs.get('registration_pre', False): pipeline_gt.add_filter(fltr_prep.ImageRegistration()) pipeline_gt.set_param( fltr_prep.ImageRegistrationParameters(atlas_t1, img.transformation, True), len(pipeline_gt.filters) - 1) # execute pipeline on the ground truth image img.images[structure.BrainImageTypes.GroundTruth] = pipeline_gt.execute( img.images[structure.BrainImageTypes.GroundTruth]) # update image properties to atlas image properties after registration img.image_properties = conversion.ImageProperties( img.images[structure.BrainImageTypes.T1w]) # extract the features feature_extractor = FeatureExtractor(img, **kwargs) img = feature_extractor.execute() img.feature_images = { } # we free up memory because we only need the img.feature_matrix # for training of the classifier return img