fast-mri/uncertainty/scripts/select_data_umcg_old.py

49 lines
1.7 KiB
Python

import glob
import csv
df_list = []
ROOT_DIR = '../../../datasets/anonymized_mri/only_nii_directory/*/*'
x = glob.glob(ROOT_DIR)
for patient_dir in sorted(x):
T2 = []
adc = []
highb = []
patient_id = []
age_year = []
patient_id = patient_dir.split('/')[-2]
age_year = patient_dir.split('/')[-1]
for path in sorted(glob.glob(f'../../../datasets/anonymized_mri/only_nii_directory/{patient_dir.split("/")[-2]}/{patient_dir.split("/")[-1]}/*/*')):
if ('T2' in path or 't2' in path) and ('tra' in path or 'TRA' in path) and not ('seg' in path):
T2 = path
for path in sorted(glob.glob(f'../../../datasets/anonymized_mri/only_nii_directory/{patient_dir.split("/")[-2]}/{patient_dir.split("/")[-1]}/*')):
if 'manual_adc' in path:
manual_adc = path
else:
if 'ADC' in path and 'nii.gz' in path:
adc = path
print(path)
if 'manual_b-1400' in path:
highb1400 = path
else:
if 'b-2000' in path and 'nii.gz' in path:
highb = path
print(path)
if manual_adc:
adc = manual_adc
if highb1400:
highb = highb1400
df_list.append({'patient_id':patient_id,'age_year':age_year,'t2':T2,'adc':adc,'high_b':highb})
if not T2 or not adc or not highb:
input(f'{patient_id} {age_year} {T2} {adc} {highb}')
with open('csv_file.csv', 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=['patient_id','age_year','t2','adc','high_b'],delimiter=';')
writer.writeheader()
for data in df_list:
writer.writerow(data)