def load_cubes_in_dir(directory):
cube_list = os.listdir(directory)
cubes = []
for file in cube_list:
file_path = os.path.join(directory, file)
cubes.append(imio.load_any(file_path))
return cubes
def read(self, filename: str) -> Optional[LocalAtlasReaderData]:
path = Path(filename)
volume = imio.load_any(str(path))
return LocalAtlasReaderData(
source="File",
name=path.name,
registration_volume=volume,
)
def get_lateralised_atlas(
atlas_path,
hemispheres_path,
left_hemisphere_value=2,
right_hemisphere_value=1,
):
atlas = imio.load_any(atlas_path)
hemispheres = imio.load_any(hemispheres_path)
atlas_left, atlas_right = lateralise_atlas(
atlas,
hemispheres,
left_hemisphere_value=left_hemisphere_value,
right_hemisphere_value=right_hemisphere_value,
)
unique_vals_left, counts_left = np.unique(atlas_left, return_counts=True)
unique_vals_right, counts_right = np.unique(atlas_right,
return_counts=True)
return unique_vals_left, unique_vals_right, counts_left, counts_right
def boundaries(registered_atlas, boundaries_out_path):
"""
Generate the boundary image, which is the border between each segmentation
region. Useful for overlaying on the raw image to assess the registration
and segmentation
:param registered_atlas: The registered atlas
:param boundaries_out_path: Path to save the boundary image
"""
atlas_img = imio.load_any(registered_atlas)
boundaries_image = find_boundaries(atlas_img,
mode="inner").astype(np.int8,
copy=False)
logging.debug("Saving segmentation boundary image")
imio.to_tiff(
boundaries_image,
boundaries_out_path,
)
def run_niftyreg(
registration_output_folder,
paths,
atlas,
atlas_pixel_sizes,
target_brain,
n_processes,
additional_images_downsample,
DATA_ORIENTATION,
ATLAS_ORIENTATION,
niftyreg_args,
x_scaling,
y_scaling,
z_scaling,
load_parallel,
sort_input_file,
n_free_cpus,
debug=False,
):
niftyreg_directory = os.path.join(registration_output_folder, "niftyreg")
niftyreg_paths = NiftyRegPaths(niftyreg_directory)
save_nii(atlas.hemispheres, atlas_pixel_sizes, niftyreg_paths.hemispheres)
save_nii(atlas.annotation, atlas_pixel_sizes, niftyreg_paths.annotations)
reference = preprocess.filter_image(atlas.reference)
save_nii(reference, atlas_pixel_sizes, niftyreg_paths.brain_filtered)
save_nii(target_brain, atlas_pixel_sizes, niftyreg_paths.downsampled_brain)
imio.to_tiff(target_brain, paths.downsampled_brain_path)
target_brain = preprocess.filter_image(target_brain)
save_nii(
target_brain,
atlas_pixel_sizes,
niftyreg_paths.downsampled_filtered,
)
logging.info("Registering")
registration_params = RegistrationParams(
affine_n_steps=niftyreg_args.affine_n_steps,
affine_use_n_steps=niftyreg_args.affine_use_n_steps,
freeform_n_steps=niftyreg_args.freeform_n_steps,
freeform_use_n_steps=niftyreg_args.freeform_use_n_steps,
bending_energy_weight=niftyreg_args.bending_energy_weight,
grid_spacing=niftyreg_args.grid_spacing,
smoothing_sigma_reference=niftyreg_args.smoothing_sigma_reference,
smoothing_sigma_floating=niftyreg_args.smoothing_sigma_floating,
histogram_n_bins_floating=niftyreg_args.histogram_n_bins_floating,
histogram_n_bins_reference=niftyreg_args.histogram_n_bins_reference,
)
brain_reg = BrainRegistration(
niftyreg_paths,
registration_params,
n_processes=n_processes,
)
logging.info("Starting affine registration")
brain_reg.register_affine()
logging.info("Starting freeform registration")
brain_reg.register_freeform()
logging.info("Starting segmentation")
brain_reg.segment()
logging.info("Segmenting hemispheres")
brain_reg.register_hemispheres()
logging.info("Generating inverse (sample to atlas) transforms")
brain_reg.generate_inverse_transforms()
logging.info("Transforming image to standard space")
brain_reg.transform_to_standard_space(
niftyreg_paths.downsampled_brain,
niftyreg_paths.downsampled_brain_standard_space,
)
logging.info("Generating deformation field")
brain_reg.generate_deformation_field(niftyreg_paths.deformation_field)
logging.info("Exporting images as tiff")
imio.to_tiff(
imio.load_any(niftyreg_paths.registered_atlas_path).astype(np.uint32,
copy=False),
paths.registered_atlas,
)
imio.to_tiff(
imio.load_any(niftyreg_paths.registered_hemispheres_img_path).astype(
np.uint8, copy=False),
paths.registered_hemispheres,
)
imio.to_tiff(
imio.load_any(niftyreg_paths.downsampled_brain_standard_space).astype(
np.uint16, copy=False),
paths.downsampled_brain_standard_space,
)
del atlas
del reference
del target_brain
deformation_image = imio.load_any(niftyreg_paths.deformation_field)
imio.to_tiff(
deformation_image[..., 0, 0].astype(np.uint32, copy=False),
paths.deformation_field_0,
)
imio.to_tiff(
deformation_image[..., 0, 1].astype(np.uint32, copy=False),
paths.deformation_field_1,
)
imio.to_tiff(
deformation_image[..., 0, 2].astype(np.uint32, copy=False),
paths.deformation_field_2,
)
if additional_images_downsample:
logging.info("Saving additional downsampled images")
for name, filename in additional_images_downsample.items():
logging.info(f"Processing: {name}")
downsampled_brain_path = os.path.join(registration_output_folder,
f"downsampled_{name}.tiff")
tmp_downsampled_brain_path = os.path.join(
niftyreg_paths.niftyreg_directory,
f"downsampled_{name}.nii",
)
downsampled_brain_standard_path = os.path.join(
registration_output_folder,
f"downsampled_standard_{name}.tiff")
tmp_downsampled_brain_standard_path = os.path.join(
niftyreg_paths.niftyreg_directory,
f"downsampled_standard_{name}.nii",
)
# do the tiff part at the beginning
downsampled_brain = imio.load_any(
filename,
x_scaling,
y_scaling,
z_scaling,
load_parallel=load_parallel,
sort_input_file=sort_input_file,
n_free_cpus=n_free_cpus,
)
downsampled_brain = bg.map_stack_to(
DATA_ORIENTATION, ATLAS_ORIENTATION,
downsampled_brain).astype(np.uint16, copy=False)
save_nii(
downsampled_brain,
atlas_pixel_sizes,
tmp_downsampled_brain_path,
)
imio.to_tiff(downsampled_brain, downsampled_brain_path)
logging.info("Transforming to standard space")
brain_reg.transform_to_standard_space(
tmp_downsampled_brain_path,
tmp_downsampled_brain_standard_path,
)
imio.to_tiff(
imio.load_any(tmp_downsampled_brain_standard_path).astype(
np.uint16, copy=False),
downsampled_brain_standard_path,
)
if not debug:
logging.info("Deleting intermediate niftyreg files")
delete_directory_contents(niftyreg_directory)
os.rmdir(niftyreg_directory)
def main(
atlas,
data_orientation,
target_brain_path,
paths,
niftyreg_args,
x_pixel_um=0.02,
y_pixel_um=0.02,
z_pixel_um=0.05,
n_free_cpus=2,
sort_input_file=False,
additional_images_downsample=None,
backend="niftyreg",
debug=False,
):
n_processes = get_num_processes(min_free_cpu_cores=n_free_cpus)
load_parallel = n_processes > 1
# TODO: check orientation of atlas voxel sizes
atlas_pixel_sizes = {
"x": atlas.metadata["resolution"][0],
"y": atlas.metadata["resolution"][1],
"z": atlas.metadata["resolution"][2],
}
scaling_rounding_decimals = 5
x_scaling = round(x_pixel_um / atlas_pixel_sizes["x"],
scaling_rounding_decimals)
y_scaling = round(y_pixel_um / atlas_pixel_sizes["y"],
scaling_rounding_decimals)
z_scaling = round(z_pixel_um / atlas_pixel_sizes["z"],
scaling_rounding_decimals)
logging.info("Loading raw image data")
target_brain = imio.load_any(
target_brain_path,
x_scaling,
y_scaling,
z_scaling,
load_parallel=load_parallel,
sort_input_file=sort_input_file,
n_free_cpus=n_free_cpus,
)
target_brain = bg.map_stack_to(data_orientation,
atlas.metadata["orientation"], target_brain)
if backend == "niftyreg":
run_niftyreg(
paths.registration_output_folder,
paths,
atlas,
atlas_pixel_sizes,
target_brain,
n_processes,
additional_images_downsample,
data_orientation,
atlas.metadata["orientation"],
niftyreg_args,
x_scaling,
y_scaling,
z_scaling,
load_parallel,
sort_input_file,
n_free_cpus,
debug=debug,
)
logging.info("Calculating volumes of each brain area")
calculate_volumes(
atlas,
paths.registered_atlas,
paths.registered_hemispheres,
paths.volume_csv_path,
# for all brainglobe atlases
left_hemisphere_value=1,
right_hemisphere_value=2,
)
logging.info("Generating boundary image")
boundaries(
paths.registered_atlas,
paths.boundaries_file_path,
)
logging.info(f"brainreg completed. Results can be found here: "
f"{paths.registration_output_folder}")
def main(
atlas,
data_orientation,
target_brain_path,
paths,
voxel_sizes,
niftyreg_args,
n_free_cpus=2,
sort_input_file=False,
additional_images_downsample=None,
backend="niftyreg",
scaling_rounding_decimals=5,
debug=False,
):
atlas = BrainGlobeAtlas(atlas)
source_space = bg.AnatomicalSpace(data_orientation)
scaling = []
for idx, axis in enumerate(atlas.space.axes_order):
scaling.append(
round(
float(voxel_sizes[idx]) /
atlas.resolution[atlas.space.axes_order.index(
source_space.axes_order[idx])],
scaling_rounding_decimals,
))
n_processes = get_num_processes(min_free_cpu_cores=n_free_cpus)
load_parallel = n_processes > 1
logging.info("Loading raw image data")
target_brain = imio.load_any(
target_brain_path,
scaling[1],
scaling[2],
scaling[0],
load_parallel=load_parallel,
sort_input_file=sort_input_file,
n_free_cpus=n_free_cpus,
)
target_brain = bg.map_stack_to(data_orientation,
atlas.metadata["orientation"], target_brain)
if backend == "niftyreg":
run_niftyreg(
paths.registration_output_folder,
paths,
atlas,
target_brain,
n_processes,
additional_images_downsample,
data_orientation,
atlas.metadata["orientation"],
niftyreg_args,
scaling,
load_parallel,
sort_input_file,
n_free_cpus,
debug=debug,
)
logging.info("Calculating volumes of each brain area")
calculate_volumes(
atlas,
paths.registered_atlas,
paths.registered_hemispheres,
paths.volume_csv_path,
# for all brainglobe atlases
left_hemisphere_value=1,
right_hemisphere_value=2,
)
logging.info("Generating boundary image")
boundaries(
paths.registered_atlas,
paths.boundaries_file_path,
)
logging.info(f"brainreg completed. Results can be found here: "
f"{paths.registration_output_folder}")