commit before migration to habrok

uncertainty/scripts/select_data_umcg_new.py

@@ -0,0 +1,91 @@
+import glob
+import csv 
+from sqlite3 import Error
+import sqlite3
+
+
+def create_connection(db_file):
+    """ create a database connection to the SQLite database
+        specified by the db_file
+    :param db_file: database file
+    :return: Connection object or None
+    """
+    conn = None
+    try:
+        conn = sqlite3.connect(db_file)
+    except Error as e:
+        print(e)
+
+    return conn
+
+## get pat_id, birthdate, PSA, PIRADS, 
+conn = create_connection('./db/master.db')
+cur = conn.cursor()
+query_pirads = f"""SELECT visits.patientID, visits.StudyDate, rois.roiID FROM biopsies 
+         INNER JOIN rois ON biopsies.target_visitID = rois.StudyInstanceUID AND biopsies.target_roi = rois.roiID
+         INNER JOIN visits ON biopsies.target_visitID = visits.StudyInstanceUID
+         WHERE pirads != "NA" AND (pirads > 3);"""
+result_pirads = cur.execute(query_pirads).fetchall() #list of tuples
+dates = [f"{date[1][:4]}-{date[1][4:6]}-{date[1][6:8]}" for date in result_pirads]
+pirads_paths = [path.join(x[0],y,x[2]) for x,y in zip(result_pirads,dates) ]
+
+query_gleason = f"""SELECT visits.patientID, visits.StudyDate, rois.roiID FROM biopsies 
+         INNER JOIN rois ON biopsies.target_visitID = rois.StudyInstanceUID AND biopsies.target_roi = rois.roiID
+         INNER JOIN visits ON biopsies.target_visitID = visits.StudyInstanceUID
+         WHERE gleason_1 != "NA" AND (gleason_1 > 3 OR gleason_2 > 3);"""
+result_gleason = cur.execute(query_gleason).fetchall() #list of tuples
+dates = [f"{date[1][:4]}-{date[1][4:6]}-{date[1][6:8]}" for date in result_gleason]
+gleason_paths = [path.join(x[0],y,x[2]) for x,y in zip(result_gleason,dates) ]
+
+print("Looking for visit folders containing niftis in ./nifti")
+niftis = glob("./nifti/**/*.nii.gz", recursive=True)
+folders = list(set([path.dirname(f) for f in niftis]))
+
+print("Found", len(folders), "folders")
+
+
+# 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)

uncertainty/scripts/select_data_umcg_old.py

@@ -0,0 +1,48 @@
+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)