def import_segmentations(folder, image_object, roi_names):
# Check folder contents, keep only files that are recognised as DICOM images or other image files.
file_list = os.listdir(folder)
file_list = [file_name for file_name in file_list if not os.path.isdir(os.path.join(folder, file_name))]
# Find DICOM files
dcm_file_list = [file_name for file_name in file_list if file_name.lower().endswith(".dcm")]
# Find other image formats
other_file_list = [file_name for file_name in file_list if file_name.lower().endswith((".nii", ".nii.gz", ".nrrd"))]
if len(dcm_file_list) > 0:
# Attempt to obtain segmentations from DICOM
roi_list = read_dicom_rt_struct(dcm_folder=folder, image_object=image_object, roi=roi_names)
if len(roi_list) == 0 and len(other_file_list) > 0:
# Attempt to obtain segmentation masks from other types of image file.
roi_list = read_itk_segmentations(image_folder=folder, roi=roi_names)
elif len(other_file_list) > 0:
# Attempt to obtain segmentation masks from other types of image file.
roi_list = read_itk_segmentations(image_folder=folder, roi=roi_names)
else:
roi_list = []
if len(roi_list) == 0:
warnings.warn(f"No segmentations were imported from {folder}. This could be because the folder does not contain segmentations,"
f"or none of the segmentations matches the roi_names argument.")
return roi_list
def find_imaging_parameters(image_folder, modality, subject, plot_images, write_folder,
roi_folder=None, registration_image_folder=None, settings=None, roi_names=None):
"""
:param image_folder: path; path to folder containing image data.
:param modality: string; identifies modality of the image in the image folder.
:param subject: string; name of the subject.
:param plot_images: bool; flag to set image extraction. An image is created at the center of each ROI.
:param write_folder: path; path to folder where the analysis should be written.
:param roi_folder: path; path to folder containing the region of interest definitions.
:param registration_image_folder: path; path to folder containing image data on which the region of interest was originally created. If None, it is assumed that the image in
image_folder was used to the define the roi.
:param settings:
:param roi_names:
:return:
"""
# TODO: make it so that advanced meta-data can actually be obtained.
from mirp.imagePlot import plot_image
from mirp.imageMetaData import get_meta_data
from mirp.imageProcess import estimate_image_noise
# Read DICOM series
img_obj: ImageClass = read_dicom_image_series(image_folder=image_folder, modality=modality)
# Load registration image
if registration_image_folder == image_folder or registration_image_folder is None:
img_reg_obj = img_obj
else:
img_reg_obj: ImageClass = read_dicom_image_series(image_folder=image_folder, modality=modality)
# Load segmentations
roi_list = read_dicom_rt_struct(dcm_folder=roi_folder, image_object=img_reg_obj, roi=roi_names)
def find_imaging_parameters(image_folder,
modality,
subject,
plot_images,
write_folder,
roi_folder=None,
registration_image_folder=None,
settings=None,
roi_names=None):
"""
:param image_folder: path; path to folder containing image data.
:param modality: string; identifies modality of the image in the image folder.
:param subject: string; name of the subject.
:param plot_images: bool; flag to set image extraction. An image is created at the center of each ROI.
:param write_folder: path; path to folder where the analysis should be written.
:param roi_folder: path; path to folder containing the region of interest definitions.
:param registration_image_folder: path; path to folder containing image data on which the region of interest was
originally created. If None, it is assumed that the image in image_folder was used to the define the roi.
:param settings:
:param roi_names:
:return:
"""
from mirp.imagePlot import plot_image
from mirp.imageMetaData import get_meta_data
from mirp.imageProcess import estimate_image_noise
# Read DICOM series
img_obj: ImageClass = read_dicom_image_series(image_folder=image_folder,
modality=modality)
# Load registration image
if registration_image_folder == image_folder or registration_image_folder is None:
img_reg_obj = img_obj
else:
img_reg_obj: ImageClass = read_dicom_image_series(
image_folder=image_folder, modality=modality)
# Load segmentations
roi_list = read_dicom_rt_struct(dcm_folder=roi_folder,
image_object=img_reg_obj,
roi=roi_names)
# Load dicom headers for all slices in the image object.
dcm_list = get_all_dicom_headers(
image_folder=image_folder,
modality=modality,
sop_instance_uid=img_obj.slice_table.sop_instance_uid.values)
# Parse metadata
metadata_table = get_meta_data(dcm_list=dcm_list, modality=modality)
# Add sample identifier, folder and image noise
metadata_table["subject"] = subject
metadata_table["folder"] = image_folder
metadata_table["noise"] = estimate_image_noise(img_obj=img_obj,
settings=None,
method="chang")
# Find the segmentation range.
if settings is None:
g_range = None
else:
g_range = settings.roi_resegment.g_thresh
# Plot images
if isinstance(plot_images, str):
if plot_images == "single":
plot_image(img_obj=img_obj,
roi_list=roi_list,
slice_id="roi_center",
file_path=write_folder,
file_name=subject + "_" + modality,
g_range=g_range)
elif plot_images == "all_roi":
plot_image(img_obj=img_obj,
roi_list=roi_list,
slice_id="all_roi",
file_path=write_folder,
file_name=subject + "_" + modality,
g_range=g_range)
elif plot_images == "all":
plot_image(img_obj=img_obj,
roi_list=roi_list,
slice_id="all",
file_path=write_folder,
file_name=subject + "_" + modality,
g_range=g_range)
elif isinstance(plot_images, bool):
if plot_images:
plot_image(img_obj=img_obj,
roi_list=roi_list,
slice_id="roi_center",
file_path=write_folder,
file_name=subject + "_" + modality,
g_range=settings.roi_resegment.g_thresh)
else:
raise TypeError("plot_image is expected to be a string or boolean.")
# Write table to single file for case-by-case analysis
metadata_table.to_frame().T.to_csv(path_or_buf=os.path.normpath(
os.path.join(write_folder,
subject + "_" + modality + "_meta_data.csv")),
sep=";",
na_rep="NA",
index=False,
decimal=".")
return metadata_table.to_frame().T