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