def spatial_transformation_augmented(self, iterator, datasources, image_size):
"""
The spatial image transformation with random augmentation.
:param datasources: datasources dict.
:return: The transformation.
"""
transformation_list = []
kwparents = {'image': datasources['image'], 'output_size': image_size}
if self.translate_to_center_landmarks:
kwparents['start'] = datasources['landmarks_bb_start']
kwparents['extent'] = datasources['landmarks_bb_extent']
transformation_list.append(translation.BoundingBoxCenterToOrigin(self.dim, None, self.image_spacing))
elif self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap:
single_landmark = LambdaNode(lambda id_dict, landmarks: [landmarks[int(id_dict['landmark_id'])]],
parents=[iterator, datasources['landmarks']])
kwparents['landmarks'] = single_landmark
transformation_list.append(landmark.Center(self.dim, True))
transformation_list.append(translation.Fixed(self.dim, [0, 20, 0]))
else:
transformation_list.append(translation.InputCenterToOrigin(self.dim))
if self.translate_by_random_factor:
transformation_list.append(translation.RandomCropBoundingBox(self.dim, None, self.image_spacing))
# transformation_list.append(translation.RandomFactorInput(self.dim, [0, 0, 0.5], [0, 0, self.image_spacing[2] * image_size[2]]))
transformation_list.extend([translation.Random(self.dim, [self.random_translation] * self.dim),
rotation.Random(self.dim, [self.random_rotate] * self.dim),
scale.RandomUniform(self.dim, self.random_scale),
scale.Random(self.dim, [self.random_scale] * self.dim),
flip.Random(self.dim, [0.5 if self.random_flip else 0.0, 0.0, 0.0]),
translation.OriginToOutputCenter(self.dim, None, self.image_spacing),
deformation.Output(self.dim, [6, 6, 6], [self.random_deformation] * self.dim, None, self.image_spacing)
])
comp = composite.Composite(self.dim, transformation_list, name='image', kwparents=kwparents)
return LambdaNode(lambda comp, output_size: sitk.DisplacementFieldTransform(sitk.TransformToDisplacementField(comp, sitk.sitkVectorFloat64, size=output_size, outputSpacing=self.image_spacing)),
name='image',
kwparents={'comp': comp, 'output_size': image_size})
def dataset_val(self):
"""
Returns the validation dataset. No random augmentation is performed.
:return: The validation dataset.
"""
if self.cv == 'inference':
iterator = 'iterator'
else:
iterator = self.iterator(self.test_file, False)
sources = self.datasources(iterator, True, True, self.preprocessing, 2048)
if self.use_variable_image_size:
if self.load_spine_bbs:
image_size = ImageSizeGenerator(self.dim, [None] * 3, self.image_spacing, valid_output_sizes=[[32, 64, 96, 128], [32, 64, 96, 128], [32 + i * 32 for i in range(20)]], name='output_size', kwparents={'extent': sources['landmarks_bb_extent']})
else:
image_size = ImageSizeGenerator(self.dim, [None] * 3, self.image_spacing, valid_output_sizes=[[32, 64, 96, 128], [32, 64, 96, 128], [32 + i * 32 for i in range(20)]], name='output_size', kwparents={'image': sources['image']})
#image_size = ImageSizeGenerator(self.dim, [None] * 3, self.image_spacing, valid_output_sizes=[self.valid_output_sizes_x, self.valid_output_sizes_y, [64 + i * 32 for i in range(20)]], name='output_size', kwparents={'extent': sources['landmarks_bb_extent']})
else:
image_size = LambdaNode(lambda: self.image_size, name='output_size')
reference_transformation = self.spatial_transformation(iterator, sources, image_size)
generators = self.data_generators(iterator, sources, reference_transformation, self.postprocessing, False, image_size, False)
generators['image_id'] = LambdaNode(lambda d: np.array(d['image_id']), name='image_id', parents=[iterator])
return GraphDataset(data_generators=list(generators.values()),
data_sources=list(sources.values()),
transformations=[reference_transformation],
iterator=iterator,
debug_image_folder='debug_val' if self.save_debug_images else None)
def all_generators_post_processing(self,
generators_dict,
random_move=False):
"""
Function that will be called, after all data generators generated their data. Used to combine the results
of the individual datagenerators. This function will create a dict as follows:
{
'image': generators_dict['image'],
'instances_bac': Instance image for the background. Combines generators_dict['merged'],
generators_dict['seg_loss_mask'], and generators_dict['has_complete_seg'].
'instances_merged': generators_dict['merged']
:param generators_dict: The generators_dict that will be generated from the dataset.
:param random_move: If true, the resulting images will be moved randomly according to the given parameters.
:return: The final generators_dict of np arrays.
"""
final_generators_dict = {}
final_generators_dict['image'] = generators_dict['image']
if self.create_instances_merged:
final_generators_dict['instances_merged'] = LambdaNode(
lambda x: self.instance_image(x, self.instances_datatype),
name='instances_merged',
parents=[generators_dict['merged']])
if self.create_instances_bac:
def instances_bac(merged, seg_loss_mask, has_complete_seg):
binary_seg = self.binary_labels(merged)
seg_loss_mask = self.loss_mask(seg_loss_mask, has_complete_seg)
instances_bac = (binary_seg[0] +
2 * binary_seg[1]) * seg_loss_mask
return instances_bac.astype(np.int8)
final_generators_dict['instances_bac'] = LambdaNode(
instances_bac,
name='instances_bac',
parents=[
generators_dict['merged'],
generators_dict['seg_loss_mask'],
generators_dict['has_complete_seg']
])
if random_move:
random_move = LambdaNode(self.get_random_move,
name='random_move',
parents=[final_generators_dict['image']])
for key, value in final_generators_dict.items():
if key == 'image':
f = lambda image, random_move: self.move_volume(
image, random_move, 'reflect')
else:
f = lambda image, random_move: self.move_volume(
image, random_move, 'constant')
final_generators_dict[key] = LambdaNode(
f, parents=[value, random_move], name=key)
return final_generators_dict
def landmark_datasources(self, iterator):
"""
Returns all LandmarkDataSources as a dictionary. The entry 'landmark_data_source' is set to the value set in self.landmark_source.
Other entries: 'junior_landmarks_datasource',
'senior_landmarks_datasource',
'challenge_landmarks_datasource',
'mean_landmarks_datasource',
'random_landmarks_datasource'.
:param iterator: The iterator that is used as the parent node.
:return: Dictionary of all LandmarkDataSources.
"""
senior_landmarks_datasource = LandmarkDataSource(
self.senior_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='senior_landmarks_datasource')
junior_landmarks_datasource = LandmarkDataSource(
self.junior_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='junior_landmarks_datasource')
challenge_landmarks_datasource = LandmarkDataSource(
self.challenge_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='challenge_landmarks_datasource')
mean_landmarks_datasource = LambdaNode(
lambda junior, senior: get_mean_landmark_list(junior, senior),
parents=[junior_landmarks_datasource, senior_landmarks_datasource],
name='mean_landmarks_datasource')
random_landmarks_datasource = LambdaNode(
lambda junior, senior: senior if bool_bernoulli(0.5) else junior,
parents=[junior_landmarks_datasource, senior_landmarks_datasource],
name='random_landmarks_datasource')
if self.landmark_source == 'senior':
landmark_datasource = senior_landmarks_datasource
elif self.landmark_source == 'junior':
landmark_datasource = junior_landmarks_datasource
elif self.landmark_source == 'challenge':
landmark_datasource = challenge_landmarks_datasource
elif self.landmark_source == 'mean':
landmark_datasource = mean_landmarks_datasource
elif self.landmark_source == 'random':
landmark_datasource = random_landmarks_datasource
return {
'junior_landmarks_datasource': junior_landmarks_datasource,
'senior_landmarks_datasource': senior_landmarks_datasource,
'challenge_landmarks_datasource': challenge_landmarks_datasource,
'mean_landmarks_datasource': mean_landmarks_datasource,
'random_landmarks_datasource': random_landmarks_datasource,
'landmark_datasource': landmark_datasource
}
def spatial_transformation_volumetric_augmented(self, image):
"""
The spatial image transformation with random augmentation.
:return: The transformation.
"""
dim = 3
transformations_list = [translation.InputCenterToOrigin(dim),
scale.FitFixedAr(dim, self.image_size + [None], ignore_dim=[2])]
if self.scale_factor[0] == 1.0 and self.scale_factor[1] == 1.0:
# if no scale_factor, randomly shift by certain value
transformations_list.append(translation.Random(dim, [self.image_size[0] * 0.35, self.image_size[1] * 0.35, 0.0]))
else:
# else, randomly move in imag size
move_factor = [(1.0 - self.scale_factor[i]) * 0.5 for i in [0, 1]]
transformations_list.append(translation.Random(dim, [self.image_size[0] * move_factor[0], self.image_size[1] * move_factor[1], 0]))
transformations_list.append(scale.Fixed(dim, self.scale_factor + [1.0]))
transformations_list.extend([flip.Random(dim, [0.5, 0.5, 0.0]),
rotation.Random(dim, [0., 0., math.pi]),
scale.RandomUniform(dim, 0.25, ignore_dim=[2]),
scale.Random(dim, [0.25, 0.25, 0.0]),
translation.OriginToOutputCenter(dim, self.image_size + [self.num_frames]),
deformation.Output(dim, [6, 6, 4], [10, 10, 0], self.image_size + [self.num_frames])])
comp = composite.Composite(dim, transformations_list,
name='image_transformation_comp',
kwparents={'image': image})
return LambdaNode(lambda comp: sitk.DisplacementFieldTransform(sitk.TransformToDisplacementField(comp, sitk.sitkVectorFloat64, size=self.image_size + [self.num_frames])),
name='image_transformation',
kwparents={'comp': comp})
def spatial_transformation_augmented(self, image):
"""
The spatial image transformation with random augmentation.
:return: The transformation.
"""
# bring image to center and fit to AR
transformations_list = [translation.InputCenterToOrigin(self.dim),
scale.FitFixedAr(self.dim, self.image_size)]
if self.scale_factor[0] == 1.0 and self.scale_factor[1] == 1.0:
# if no scale_factor, randomly shift by certain value
transformations_list.append(translation.Random(self.dim, [self.image_size[0] * 0.35, self.image_size[1] * 0.35]))
else:
# else, randomly move in imag size
move_factor = [(1.0 - self.scale_factor[i]) * 0.5 for i in [0, 1]]
transformations_list.append(translation.Random(self.dim, [self.image_size[0] * move_factor[0], self.image_size[1] * move_factor[1]]))
transformations_list.append(scale.Fixed(self.dim, self.scale_factor))
transformations_list.extend([flip.Random(self.dim, [0.5, 0.5]),
rotation.Random(self.dim, [math.pi]),
scale.RandomUniform(self.dim, 0.25),
scale.Random(self.dim, [0.25, 0.25]),
translation.OriginToOutputCenter(self.dim, self.image_size),
deformation.Output(self.dim, [8, 8], 10, self.image_size)])
comp = composite.Composite(self.dim, transformations_list,
name='image_transformation_comp',
kwparents={'image': image})
return LambdaNode(lambda comp: sitk.DisplacementFieldTransform(sitk.TransformToDisplacementField(comp, sitk.sitkVectorFloat64, size=self.image_size)),
name='image_transformation',
kwparents={'comp': comp})
def datasources(self, iterator, cached):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'labels:' CachedImageDataSource that loads the groundtruth labels.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The dataset iterator.
:param cached: If true, use CachedImageDataSource, else ImageDataSource.
:return: A dict of data sources.
"""
datasources_dict = {}
image_data_source = CachedImageDataSource if cached else ImageDataSource
datasources_dict['image'] = image_data_source(
self.image_base_folder,
'',
'',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkInt16,
preprocessing=self.preprocessing,
name='image',
parents=[iterator])
if self.generate_landmark_mask:
datasources_dict['landmark_mask'] = LambdaNode(
self.landmark_mask_preprocessing,
name='image',
parents=[datasources_dict['image']])
if self.generate_labels or self.generate_single_vertebrae:
datasources_dict['labels'] = image_data_source(
self.image_base_folder,
'',
'_seg',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkUInt8,
name='labels',
parents=[iterator])
if self.generate_landmarks or self.generate_heatmaps or self.generate_spine_heatmap or self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap or (
self.translate_to_center_landmarks
and not self.load_spine_landmarks):
datasources_dict['landmarks'] = LandmarkDataSource(
self.landmarks_file,
self.num_landmarks,
self.dim,
name='landmarks',
parents=[iterator])
if self.load_spine_landmarks:
datasources_dict['spine_landmarks'] = LandmarkDataSource(
self.spine_landmarks_file,
1,
self.dim,
name='spine_landmarks',
parents=[iterator])
return datasources_dict
def spatial_transformationx(self, iterator, datasources, image_size):
"""
The spatial image transformation without random augmentation.
:param datasources: datasources dict.
:return: The transformation.
"""
transformation_list = []
kwparents = {'image': datasources['image'], 'output_size': image_size}
if self.translate_to_center_landmarks:
if 'spine_landmarks' in datasources:
kwparents['landmarks'] = datasources['spine_landmarks']
else:
kwparents['landmarks'] = datasources['landmarks']
transformation_list.append(translation.InputCenterToOrigin(self.dim, used_dimensions=[False, False, True]))
transformation_list.append(landmark.Center(self.dim, True, used_dimensions=[True, True, False]))
elif self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap:
single_landmark = LambdaNode(lambda id_dict, landmarks: [landmarks[int(id_dict['landmark_id'])]],
parents=[iterator, datasources['landmarks']])
kwparents['landmarks'] = single_landmark
transformation_list.append(landmark.Center(self.dim, True))
transformation_list.append(translation.Fixed(self.dim, [0, 20, 0]))
else:
transformation_list.append(translation.InputCenterToOrigin(self.dim))
transformation_list.append(translation.OriginToOutputCenter(self.dim, None, self.image_spacing))
comp = composite.Composite(self.dim, transformation_list, name='image', kwparents=kwparents)
return comp
def dataset_val(self):
"""
Returns the validation dataset for videos. No random augmentation is performed.
:return: The validation dataset.
"""
dim = 3
full_video_frame_list_image = VideoFrameList(
self.video_frame_list_file_name,
self.num_frames - 1,
0,
border_mode='valid',
random_start=False,
random_skip_probability=0.0)
iterator = 'image_ids'
iterator_postprocessing = LambdaNode(
lambda x: full_video_frame_list_image.get_id_dict_list(
x['video_id'], x['frame_id']),
parents=[iterator])
sources = self.datasources(iterator_postprocessing)
image_key = 'image'
image_transformation = self.spatial_transformation_volumetric(
sources[image_key])
generators = self.data_generators(dim, sources, image_transformation,
None)
final_generators = self.all_generators_post_processing(
generators, False)
return GraphDataset(data_generators=list(final_generators.values()),
data_sources=list(sources.values()),
transformations=[image_transformation],
iterator=iterator,
debug_image_folder='debug_train'
if self.save_debug_images else None)
def dataset_train(self):
"""
Returns the training dataset. Random augmentation is performed.
:return: The training dataset.
"""
iterator = self.iterator(self.train_file, True)
sources = self.datasources(iterator, False, False, self.preprocessing_random, 8192)
if self.use_variable_image_size:
image_size = ImageSizeGenerator(self.dim, [None] * 3, self.image_spacing, valid_output_sizes=[self.valid_output_sizes_x, self.valid_output_sizes_y, self.valid_output_sizes_z], name='output_size', kwparents={'extent': sources['landmarks_bb_extent']})
if self.crop_randomly_smaller:
image_size = LambdaNode(self.crop_randomly_smaller_image_size, name='output_size', parents=[image_size])
else:
image_size = LambdaNode(lambda: self.image_size, name='output_size')
reference_transformation = self.spatial_transformation_augmented(iterator, sources, image_size)
generators = self.data_generators(iterator, sources, reference_transformation, self.postprocessing_random, True, image_size, self.crop_image_top_bottom)
generators['image_id'] = LambdaNode(lambda d: np.array(d['image_id']), name='image_id', parents=[iterator])
return GraphDataset(data_generators=list(generators.values()),
data_sources=list(sources.values()),
transformations=[reference_transformation],
iterator=iterator,
debug_image_folder='debug_train' if self.save_debug_images else None)
def data_generators(self, iterator, datasources, transformation,
image_post_processing,
random_translation_single_landmark, image_size):
"""
Returns the data generators that process one input. See datasources() for dict values.
:param datasources: datasources dict.
:param transformation: transformation.
:param image_post_processing: The np postprocessing function for the image data generator.
:return: A dict of data generators.
"""
generators_dict = {}
generators_dict['image'] = ImageGenerator(
self.dim,
image_size,
self.image_spacing,
interpolator='linear',
post_processing_np=image_post_processing,
data_format=self.data_format,
resample_default_pixel_value=self.image_default_pixel_value,
name='image',
parents=[datasources['image'], transformation])
if self.generate_landmark_mask:
generators_dict['landmark_mask'] = ImageGenerator(
self.dim,
image_size,
self.image_spacing,
interpolator='nearest',
data_format=self.data_format,
resample_default_pixel_value=0,
name='landmark_mask',
parents=[datasources['landmark_mask'], transformation])
if self.generate_labels or self.generate_single_vertebrae:
generators_dict['labels'] = ImageGenerator(
self.dim,
image_size,
self.image_spacing,
interpolator='nearest',
post_processing_np=self.split_labels,
data_format=self.data_format,
name='labels',
parents=[datasources['labels'], transformation])
if self.generate_heatmaps or self.generate_spine_heatmap:
generators_dict['heatmaps'] = LandmarkGeneratorHeatmap(
self.dim,
image_size,
self.image_spacing,
sigma=self.heatmap_sigma,
scale_factor=1.0,
normalize_center=True,
data_format=self.data_format,
name='heatmaps',
parents=[datasources['landmarks'], transformation])
if self.generate_landmarks:
generators_dict['landmarks'] = LandmarkGenerator(
self.dim,
image_size,
self.image_spacing,
data_format=self.data_format,
name='landmarks',
parents=[datasources['landmarks'], transformation])
if self.generate_single_vertebrae_heatmap:
single_landmark = LambdaNode(
lambda id_dict, landmarks: landmarks[int(id_dict[
'landmark_id']):int(id_dict['landmark_id']) + 1],
name='single_landmark',
parents=[iterator, datasources['landmarks']])
if random_translation_single_landmark:
single_landmark = LambdaNode(
lambda l: [
Landmark(
l[0].coords + float_uniform(
-self.random_translation_single_landmark, self.
random_translation_single_landmark, [self.dim
]), True)
],
name='single_landmark_translation',
parents=[single_landmark])
generators_dict['single_heatmap'] = LandmarkGeneratorHeatmap(
self.dim,
image_size,
self.image_spacing,
sigma=self.heatmap_sigma,
scale_factor=1.0,
normalize_center=True,
data_format=self.data_format,
name='single_heatmap',
parents=[single_landmark, transformation])
if self.generate_single_vertebrae:
if self.data_format == 'channels_first':
generators_dict['single_label'] = LambdaNode(
lambda id_dict, images: images[int(id_dict[
'landmark_id']) + 1:int(id_dict['landmark_id']) + 2,
...],
name='single_label',
parents=[iterator, generators_dict['labels']])
else:
generators_dict['single_label'] = LambdaNode(
lambda id_dict, images: images[...,
int(id_dict['landmark_id'])
+ 1:int(id_dict[
'landmark_id']) + 2],
name='single_label',
parents=[iterator, generators_dict['labels']])
if self.generate_spine_heatmap:
generators_dict['spine_heatmap'] = LambdaNode(
lambda images: gaussian(np.sum(images,
axis=0 if self.data_format ==
'channels_first' else -1,
keepdims=True),
sigma=self.spine_heatmap_sigma),
name='spine_heatmap',
parents=[generators_dict['heatmaps']])
return generators_dict
def data_generators(self, iterator, datasources, transformation, image_post_processing, random_translation_single_landmark, image_size, crop=False):
"""
Returns the data generators that process one input. See datasources() for dict values.
:param datasources: datasources dict.
:param transformation: transformation.
:param image_post_processing: The np postprocessing function for the image data generator.
:return: A dict of data generators.
"""
generators_dict = {}
kwparents = {'output_size': image_size}
image_datasource = datasources['image'] if not crop else LambdaNode(self.landmark_based_crop, name='image_cropped', kwparents={'image': datasources['image'], 'landmarks': datasources['landmarks']})
generators_dict['image'] = ImageGenerator(self.dim,
None,
self.image_spacing,
interpolator='linear',
post_processing_np=image_post_processing,
data_format=self.data_format,
resample_default_pixel_value=self.image_default_pixel_value,
np_pixel_type=self.output_image_type,
name='image',
parents=[image_datasource, transformation],
kwparents=kwparents)
# generators_dict['image'] = ImageGenerator(self.dim,
# None,
# self.image_spacing,
# interpolator='linear',
# post_processing_np=image_post_processing,
# data_format=self.data_format,
# resample_default_pixel_value=self.image_default_pixel_value,
# np_pixel_type=self.output_image_type,
# name='image_cropped',
# parents=[LambdaNode(self.landmark_based_crop, name='image_cropped', kwparents={'image': datasources['image'], 'landmarks': datasources['landmarks']}), transformation],
# kwparents=kwparents)
if self.generate_landmark_mask:
generators_dict['landmark_mask'] = ImageGenerator(self.dim,
None,
self.image_spacing,
interpolator='nearest',
data_format=self.data_format,
resample_default_pixel_value=0,
name='landmark_mask',
parents=[datasources['landmark_mask'], transformation],
kwparents=kwparents)
if self.generate_labels:
generators_dict['labels'] = ImageGenerator(self.dim,
None,
self.image_spacing,
interpolator='nearest',
post_processing_np=self.split_labels,
data_format=self.data_format,
name='labels',
parents=[datasources['labels'], transformation],
kwparents=kwparents)
if self.generate_heatmaps or self.generate_spine_heatmap:
generators_dict['heatmaps'] = LandmarkGeneratorHeatmap(self.dim,
None,
self.image_spacing,
sigma=self.heatmap_sigma,
scale_factor=1.0,
normalize_center=True,
data_format=self.data_format,
name='heatmaps',
parents=[datasources['landmarks'], transformation],
kwparents=kwparents)
if self.generate_landmarks:
generators_dict['landmarks'] = LandmarkGenerator(self.dim,
None,
self.image_spacing,
data_format=self.data_format,
name='landmarks',
parents=[datasources['landmarks'], transformation],
kwparents=kwparents)
if self.generate_single_vertebrae_heatmap:
single_landmark = LambdaNode(lambda id_dict, landmarks: landmarks[int(id_dict['landmark_id']):int(id_dict['landmark_id']) + 1],
name='single_landmark',
parents=[iterator, datasources['landmarks']])
if random_translation_single_landmark:
single_landmark = LambdaNode(lambda l: [Landmark(l[0].coords + float_uniform(-self.random_translation_single_landmark, self.random_translation_single_landmark, [self.dim]), True)],
name='single_landmark_translation',
parents=[single_landmark])
generators_dict['single_heatmap'] = LandmarkGeneratorHeatmap(self.dim,
None,
self.image_spacing,
sigma=self.single_heatmap_sigma,
scale_factor=1.0,
normalize_center=True,
data_format=self.data_format,
np_pixel_type=self.output_image_type,
name='single_heatmap',
parents=[single_landmark, transformation],
kwparents=kwparents)
if self.generate_single_vertebrae:
if self.generate_labels:
if self.data_format == 'channels_first':
generators_dict['single_label'] = LambdaNode(lambda id_dict, images: images[int(id_dict['landmark_id']) + 1:int(id_dict['landmark_id']) + 2, ...],
name='single_label',
parents=[iterator, generators_dict['labels']])
else:
generators_dict['single_label'] = LambdaNode(lambda id_dict, images: images[..., int(id_dict['landmark_id']) + 1:int(id_dict['landmark_id']) + 2],
name='single_label',
parents=[iterator, generators_dict['labels']])
else:
labels_unsmoothed = ImageGenerator(self.dim,
None,
self.image_spacing,
interpolator='nearest',
post_processing_np=None,
data_format=self.data_format,
name='labels_unsmoothed',
parents=[datasources['labels'], transformation],
kwparents=kwparents)
generators_dict['single_label'] = LambdaNode(lambda id_dict, labels: self.split_and_smooth_single_label(labels, int(id_dict['landmark_id'])),
name='single_label',
parents=[iterator, labels_unsmoothed])
if self.generate_spine_heatmap:
generators_dict['spine_heatmap'] = LambdaNode(lambda images: normalize(gaussian(np.sum(images, axis=0 if self.data_format == 'channels_first' else -1, keepdims=True), sigma=self.spine_heatmap_sigma), out_range=(0, 1)),
name='spine_heatmap',
parents=[generators_dict['heatmaps']])
return generators_dict
def datasources(self, iterator, image_cached, labels_cached, image_preprocessing, cache_size):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'labels:' CachedImageDataSource that loads the groundtruth labels.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The dataset iterator.
:param cached: If true, use CachedImageDataSource, else ImageDataSource.
:return: A dict of data sources.
"""
datasources_dict = {}
if image_cached:
image_data_source = CachedImageDataSource
image_source_kwargs = {'cache_maxsize': cache_size}
else:
image_data_source = ImageDataSource
image_source_kwargs = {}
datasources_dict['image'] = image_data_source(self.image_base_folder,
'',
'',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkInt16,
preprocessing=image_preprocessing,
name='image',
parents=[iterator],
**image_source_kwargs)
if self.generate_landmark_mask:
datasources_dict['landmark_mask'] = LambdaNode(self.landmark_mask_preprocessing,
name='landmark_mask',
parents=[datasources_dict['image']])
if self.generate_labels or self.generate_single_vertebrae:
if labels_cached:
image_data_source = CachedImageDataSource
image_source_kwargs = {'cache_maxsize': cache_size}
else:
image_data_source = ImageDataSource
image_source_kwargs = {}
datasources_dict['labels'] = image_data_source(self.image_base_folder,
'',
'_seg',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkUInt8,
name='labels',
parents=[iterator],
**image_source_kwargs)
if self.generate_landmarks or self.generate_heatmaps or self.generate_spine_heatmap or self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap or (self.translate_to_center_landmarks and not (self.load_spine_landmarks or self.load_spine_bbs)):
datasources_dict['landmarks'] = LandmarkDataSource(self.landmarks_file,
self.num_landmarks,
self.dim,
name='landmarks',
parents=[iterator])
datasources_dict['landmarks_bb'] = LambdaNode(self.image_landmark_bounding_box, name='landmarks_bb', parents=[datasources_dict['image'], datasources_dict['landmarks']])
datasources_dict['landmarks_bb_start'] = LambdaNode(lambda x: x[0], name='landmarks_bb_start', parents=[datasources_dict['landmarks_bb']])
datasources_dict['landmarks_bb_extent'] = LambdaNode(lambda x: x[1], name='landmarks_bb_extent', parents=[datasources_dict['landmarks_bb']])
if self.load_spine_landmarks:
datasources_dict['spine_landmarks'] = LandmarkDataSource(self.spine_landmarks_file, 1, self.dim, name='spine_landmarks', parents=[iterator])
if self.load_spine_bbs:
datasources_dict['spine_bb'] = LabelDatasource(self.spine_bbs_file, name='spine_landmarks', parents=[iterator])
datasources_dict['landmarks_bb'] = LambdaNode(self.image_bounding_box, name='landmarks_bb', parents=[datasources_dict['image'], datasources_dict['spine_bb']])
datasources_dict['landmarks_bb_start'] = LambdaNode(lambda x: x[0], name='landmarks_bb_start', parents=[datasources_dict['landmarks_bb']])
datasources_dict['landmarks_bb_extent'] = LambdaNode(lambda x: x[1], name='landmarks_bb_extent', parents=[datasources_dict['landmarks_bb']])
return datasources_dict