def get_to_process_with_atlases(caps_directory: str, subjects: list,
sessions: list,
atlas_dir_path: str) -> list:
import itertools
import os
from pathlib import Path
from clinica.utils.filemanip import extract_image_ids
from clinica.utils.input_files import T1_FS_DESTRIEUX
from clinica.utils.inputs import clinica_file_reader
initial_list_to_process = []
atlas_list = []
for path in Path(atlas_dir_path).rglob("*rh*6p0.gcs"):
atlas_name = path.name.split(".")[1].split("_")[0]
atlas_list.append(atlas_name)
if os.path.isdir(caps_directory):
for atlas in atlas_list:
atlas_info = dict({
"pattern":
"t1/freesurfer_cross_sectional/sub-*_ses-*/stats/rh." +
atlas + ".stats",
"description":
atlas + "-based segmentation",
"needed_pipeline":
"t1-freesurfer",
})
t1_freesurfer_output = clinica_file_reader(
subjects, sessions, caps_directory, T1_FS_DESTRIEUX, False)
t1_freesurfer_files = clinica_file_reader(
subjects, sessions, caps_directory, atlas_info, False)
image_ids = extract_image_ids(t1_freesurfer_files)
image_ids_2 = extract_image_ids(t1_freesurfer_output)
to_process = list(set(image_ids_2) - set(image_ids))
initial_list_to_process.append(([atlas], to_process))
list_to_process = []
for i in initial_list_to_process:
if list(itertools.product(i[0], i[1])) != []:
list_to_process = list_to_process + list(
itertools.product(i[0], i[1]))
return list_to_process
def get_processed_images(caps_directory, subjects, sessions):
import os
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1W_LINEAR_CROPPED
from clinica.utils.filemanip import extract_image_ids
image_ids = []
if os.path.isdir(caps_directory):
cropped_files = clinica_file_reader(subjects, sessions,
caps_directory,
T1W_LINEAR_CROPPED, False)
image_ids = extract_image_ids(cropped_files)
return image_ids
def get_processed_images(caps_directory, subjects, sessions):
import os
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1_FS_DESTRIEUX
from clinica.utils.filemanip import extract_image_ids
image_ids = []
if os.path.isdir(caps_directory):
t1_freesurfer_files = clinica_file_reader(subjects, sessions,
caps_directory,
T1_FS_DESTRIEUX, False)
image_ids = extract_image_ids(t1_freesurfer_files)
return image_ids
def get_processed_images(caps_directory, subjects, sessions):
import os
from clinica.utils.filemanip import extract_image_ids
from clinica.utils.input_files import DWI_PREPROC_NII
from clinica.utils.inputs import clinica_file_reader
image_ids = []
if os.path.isdir(caps_directory):
preproc_files = clinica_file_reader(subjects, sessions,
caps_directory,
DWI_PREPROC_NII, False)
image_ids = extract_image_ids(preproc_files)
return image_ids
def get_processed_images(caps_directory, subjects, sessions):
import os
from clinica.utils.filemanip import extract_image_ids
from clinica.utils.inputs import clinica_file_reader
information = {
"pattern":
os.path.join(
"t1_extensive",
"*_*_space-Ixi549Space_desc-SkullStripped_T1w.nii*",
),
"description":
"Skull-stripped T1w in Ixi549Space space.",
"needed_pipeline":
"t1-volume-tissue-segmentation",
}
image_ids = []
if os.path.isdir(caps_directory):
skull_stripped_files = clinica_file_reader(subjects, sessions,
caps_directory,
information, False)
image_ids = extract_image_ids(skull_stripped_files)
return image_ids