def parse_xml_file(file):
# Create an XML Tree, use own method to remove namespaces
root = xmlHelper.create_xml_tree(file)
# IPython.embed()
# Find the Study and Series IDs if possible
study_uid = xmlHelper.get_study_uid(root)
series_uid = xmlHelper.get_series_uid(root)
print("Parsing %s" % file)
print("Get study_uid %s and series_uid %s" % (study_uid, series_uid))
if study_uid is None:
write_error("Failed to find Study UID: " + file)
return
# Find the DICOMS matching the study and series ID.
# Assuming that all DICOMS to a study/series ID are in one folder.
dicom_path, no_of_dicoms = get_dicom_from_study_uid(study_uid, series_uid)
if no_of_dicoms < 10:
print(dicom_path)
print("No DICOM's found for file:", file)
return
print(dicom_path)
# IPython.embed()
# Files are saved in a folder with the structure $PatientID/$StudyID/$SeriesID/$DicomName
# Removing StudyID, SeriesID and DICOM-Name gives a patient ID --> StudyUID
# long_patient_id=os.path.basename(os.path.dirname(os.path.dirname(os.path.dirname(dicom_path))))
patient_id = study_uid
# For some patients, more than one scan is provided (for example due to multiple
# time points). To ensure that each time point is only scanned one, an appendix
# is added to the patient_id, ensuring that multiple time points can be selected.
# for appendix in list_of_appendix:
# target_path = os.path.join(path_to_nrrds, patient_id+appendix)
# if not os.path.exists(target_path):
appendix = file[-5]
patient_id = patient_id + appendix
print(patient_id)
# break
# Create Nrrd files from DICOMS and reading spacing and orgiin.
nrrd_file = create_nrrd_from_dicoms(dicom_path, patient_id)
spacing, origin = get_spacing_and_origin(nrrd_file)
# Creating multiple planar figures for each reading session.
# Each session represents the result of an different expert
# Each session gets an session ID that is unique for the given patient ID
# Same session ids for differnt patients do not necessarily correspond to the same expert.
print("Creating Planar Figure")
session_id = 0
for session in root.iter('readingSession'):
create_planarfigure_for_session(session, spacing, origin, patient_id,
session_id)
session_id = session_id + 1
convert_planar_figures_to_masks(nrrd_file, patient_id)
print("Merging Planar Figures")
merge_planar_figures_per_nodule(nrrd_file, patient_id)
def parse_xml_file(file):
# Create an XML Tree, use own method to remove namespaces
root=xmlHelper.create_xml_tree(file)
# IPython.embed()
# Find the Study and Series IDs if possible
study_uid=xmlHelper.get_study_uid(root)
series_uid=xmlHelper.get_series_uid(root)
print("Parsing %s" % file)
print("Get study_uid %s and series_uid %s" % (study_uid, series_uid))
if study_uid is None:
write_error("Failed to find Study UID: " + file)
return
# Find the DICOMS matching the study and series ID.
# Assuming that all DICOMS to a study/series ID are in one folder.
dicom_path, no_of_dicoms=get_dicom_from_study_uid(study_uid, series_uid)
if no_of_dicoms < 10:
print(dicom_path)
print("No DICOM's found for file:",file)
return
print(dicom_path)
# IPython.embed()
# Files are saved in a folder with the structure $PatientID/$StudyID/$SeriesID/$DicomName
# Removing StudyID, SeriesID and DICOM-Name gives a patient ID --> StudyUID
# long_patient_id=os.path.basename(os.path.dirname(os.path.dirname(os.path.dirname(dicom_path))))
patient_id=study_uid
# For some patients, more than one scan is provided (for example due to multiple
# time points). To ensure that each time point is only scanned one, an appendix
# is added to the patient_id, ensuring that multiple time points can be selected.
# for appendix in list_of_appendix:
# target_path = os.path.join(path_to_nrrds, patient_id+appendix)
# if not os.path.exists(target_path):
appendix=file[-5]
patient_id =patient_id+appendix
print(patient_id)
# break
# Create Nrrd files from DICOMS and reading spacing and orgiin.
nrrd_file=create_nrrd_from_dicoms(dicom_path, patient_id)