push before migration

reader_study/PSA.txt

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+4
+4
+18
+4
+20
+6
+12
+1
+0
+20
+11
+9
+None
+12
+18
+7
+0
+11
+14
+9
+7
+8
+8
+17
+13
+1
+8
+10
+1
+0

reader_study/age.txt

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+62
+56
+67
+49
+66
+67
+66
+77
+74
+77
+73
+74
+69
+51
+71
+69
+71
+72
+78
+70
+77
+56
+56
+64
+77
+54
+63
+69
+49
+69

reader_study/current_max_PIRADS.txt

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+2
+2
+1
+2
+2
+4
+0
+5
+3
+2
+4
+5
+2
+5
+2
+4
+3
+5
+4
+3
+2
+2
+0
+4
+5
+5
+2
+2
+3
+5

reader_study/locations.txt

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+['PZ']
+['TZ', 'PZ', 'TZ', 'PZ', '', 'PZ', 'PZ']
+['TZ']
+['']
+['TZ']
+['', '', 'PZ', 'CZ', 'CZ']
+[]
+['PZ']
+['TZ', 'PZ', 'PZ', 'TZ', 'PZ', 'AS', 'AS']
+['PZ']
+['PZ']
+['TZ', 'PZ', 'PZ']
+['PZ']
+['PZ']
+['PZ', 'TZ']
+['PZ']
+['PZ']
+['TZ', 'TZ', 'PZ', 'AS']
+['CZ', 'PZ']
+['TZ']
+['TZ']
+['PZ', 'PZ']
+[]
+['PZ']
+['AS']
+['PZ']
+['PZ']
+['PZ']
+['PZ', 'PZ', 'PZ']
+['CZ', 'PZ']

reader_study/make_data_paths.py

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+import pandas
+import numpy as np 
+import os 
+from sfransen.DWI_exp.helpers import *
+from sfransen.utils_quintin import *
+from os import path
+import SimpleITK as sitk
+import xml.etree.ElementTree as ET
+import pathlib
+
+def parse_marklist(marklistpath):
+    tree = ET.parse(marklistpath)
+    root = tree.getroot()
+    patient_element = (list(root.iter("markpatient")) + [None])[0]
+    PSA = patient_element.find("PSA").text if (patient_element is not None and patient_element.find("PSA") is not None) else 0
+    number_of_lesions = []
+    locations =[]
+    current_max_PIRADS = 0
+    for mark in root.iter("mark"):
+        PIRADS = mark.find("PIRADS").text if mark.find("PIRADS") is not None else 0
+        if int(PIRADS) > 0:
+            number_of_lesions.append(PIRADS)
+        if int(PIRADS) > int(current_max_PIRADS):
+            current_max_PIRADS = PIRADS
+        
+        location = mark.find("Zones/Zone/Type")
+        if location is not None:
+            location = location.text
+        else:
+            location = ''
+        locations.append(location)
+
+        # lesions_ = 1 if mark.find("PIRADS") is not None else 0
+        # number_of_lesions += number_of_lesions + lesions_
+    # if current_max_PIRADS == 0:
+    #     if len(number_of_lesions) > 0:
+    #         print(f'no PIRADS, wel lesie {number_of_lesions}')
+
+    return PSA, current_max_PIRADS, number_of_lesions, locations
+
+def parse_age(path):
+    tree = ET.parse(path)
+    root = tree.getroot()
+    age = root[6].text
+    return age[1:-1]
+
+DATA_DIR = "./../data/Nijmegen paths/"
+OUTPUT_DIR = "./random_images_2/"
+
+with open(path.join(DATA_DIR, "t2.txt"), 'r') as f:
+    pat_ids = [l.split('/')[5] for l in f.readlines()]
+with open(path.join(DATA_DIR, "t2.txt"), 'r') as f:
+    years = [l.split('/')[6] for l in f.readlines()]
+
+PSA_list = []
+current_max_PIRADS_list = []
+number_of_lesions_list = []
+age_list = []
+pat_id_list = []
+locations_list = []
+for img in ['t2','adccalc2','adccalc3','b1400calc2','b1400calc3']:
+    with open(path.join(DATA_DIR, f"{img}.txt"), 'r') as f:
+        image_paths = [l.strip() for l in f.readlines()]
+    for idx in [23,161,543,734,367, 85, 380, 231, 406, 435, 660, 327, 305, 7, 479, 540, 558, 361, 167, 320, 666, 178, 700, 831, 707, 596, 715, 823, 561, 782]:
+        print(idx)
+        read_img = sitk.ReadImage(image_paths[idx],sitk.sitkFloat32)
+        marklistpath = f'../../datasets/radboud_new/{pat_ids[idx]}/{years[idx]}/markdatasetlist.xml'
+        info_age = f'../../datasets/radboud_new/{pat_ids[idx]}/{years[idx]}/t2_tse_tra/info.xml'
+        PSA, current_max_PIRADS, number_of_lesions, locations = parse_marklist(marklistpath)
+        age = parse_age(info_age)
+        pat_id_list.append(str(pat_ids[idx]))
+        if img == 't2':
+            PSA_list.append(PSA)
+            current_max_PIRADS_list.append(current_max_PIRADS)
+            number_of_lesions_list.append(list(number_of_lesions))
+            age_list.append(age)
+            locations_list.append(locations)
+
+            # if len(number_of_lesions) > 2:
+                # print(f'number_of_lesion {number_of_lesions}')
+                # input(f'current_max_PIRADS {current_max_PIRADS}')
+                # input(f'PSA {PSA}')
+                # input(f'age {age}')
+        # method 1: all bvalues, method 2: omitting b800
+        if img == 't2':
+            name = f'{pat_ids[idx]}-t2'
+        if img == 'adccalc2':
+            name = f'{pat_ids[idx]}-adc_method2'
+        if img == 'adccalc3':
+            name = f'{pat_ids[idx]}-adc_method1'
+        if img == 'b1400calc2':
+            name = f'{pat_ids[idx]}-dwi_method2'
+        if img == 'b1400calc3':
+            name = f'{pat_ids[idx]}-dwi_method1'
+
+        # sitk.WriteImage(read_img,f'{OUTPUT_DIR}{name}.nii.gz')
+
+for idx in [23,161,543,734,367, 85, 380, 231, 406, 435, 660, 327, 305, 7, 479, 540, 558, 361, 167, 320, 666, 178, 700, 831, 707, 596, 715, 823, 561, 782]:
+
+
+with open(f'./PSA.txt', 'w') as f:
+    for line in PSA_list:
+        f.write(str(line))
+        f.write('\n')
+with open(f'./age.txt', 'w') as f:
+    for line in age_list:
+        f.write(str(line))
+        f.write('\n')
+input(number_of_lesions_list)
+with open(f'./number_of_lesions.txt', 'w') as f:
+    for line in number_of_lesions_list:
+        f.write(str(line))
+        f.write('\n')
+with open(f'./current_max_PIRADS.txt', 'w') as f:
+    for line in current_max_PIRADS_list:
+        f.write(str(line))
+        f.write('\n')
+with open(f'./pat_ids.txt', 'w') as f:
+    for line in pat_id_list:
+        f.write(str(line))
+        f.write('\n')
+with open(f'./locations.txt', 'w') as f:
+    for line in locations_list:
+        f.write(str(line))
+        f.write('\n')
+exit()
+# Read and preprocess each of the paths for each series, and the segmentations.
+for img_idx in tqdm(range(num_images)): #[:20]): #for less images
+    img_s = {s: sitk.ReadImage(image_paths[s][img_idx], sitk.sitkFloat32) 
+        for s in args.series}
+    seg_s = sitk.ReadImage(seg_paths[img_idx], sitk.sitkFloat32)
+    img_n, seg_n = preprocess(img_s, seg_s, 
+        shape=IMAGE_SHAPE, spacing=TARGET_SPACING)
+    for seq in img_n:
+        images[seq].append(img_n[seq])
+    segmentations.append(seg_n)
+
+# Split train and validation 
+# We use KFold to split the data, but we don't actually do cross validation, we 
+# just use it to split the data 1:9.
+# kfold = KFold(10, shuffle=True, random_state=123)
+# train_idxs, valid_idxs = list(kfold.split(segmentations))[0]
+# train_idxs = list(train_idxs)
+# valid_idxs = list(valid_idxs)
+
+yml_paths = read_yaml_to_dict(f'./../data/Nijmegen paths/train_val_test_idxs_{args.fold}.yml')
+print('test, train paths',yml_paths)
+train_idxs = yml_paths['train_set0']
+valid_idxs = yml_paths['val_set0']
+
+
+df = pandas.read_csv('./marksheet_with_gleason.csv')
+gleason_idxs = [idx for idx, i in enumerate(df['lesion_GS'].values) if not pandas.isna(i)]
+values = [f"{df['patient_id'].values[i]}_{df['study_id'].values[i]}" for i in gleason_idxs]
+print(values)
+
+# read picai paths
+files = ['picai_seg_list','picai_adc_list','picai_hbv_list','picai_t2_list']
+for file in files:
+    image_paths = []
+    with open(f"../../../../datasets/picai/{file}.txt") as f:
+        image_paths = [l.strip() for l in f.readlines()]
+        
+    if file is 'picai_seg_list':
+        image_paths_gleason = [image_path for image_path in image_paths if os.path.basename(image_path[:-7]) in values]
+    else:
+        image_paths_gleason = [image_path for image_path in image_paths if os.path.basename(image_path[:-8]) in values]
+    print(len(image_paths_gleason))
+
+    with open(f'./{file}.txt', 'w') as f:
+        for line in image_paths_gleason:
+            f.write(line)
+            f.write('\n')
+

reader_study/number_of_lesions.txt

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+['2']
+['2', '2', '2', '2', '2', '2', '2']
+['1']
+['2']
+['2']
+['4', '2', '2', '2']
+[]
+['5']
+['2', '3', '2', '2', '2', '3', '2']
+['2']
+['4']
+['4', '5', '2']
+['2']
+['5']
+['2', '2']
+['4']
+['3']
+['2', '2', '4', '5']
+['4', '4']
+['3']
+['2']
+['2', '2']
+[]
+['4']
+['5']
+['5']
+['2']
+['2']
+['2', '3', '2']
+['5', '4']

reader_study/pat_ids.txt

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+pat0744
+pat0587
+pat0607
+pat0092
+pat0600
+pat1022
+pat0355
+pat0836
+pat1011
+pat0337
+pat0693
+pat0216
+pat0687
+pat0124
+pat0779
+pat0833
+pat0197
+pat0121
+pat0597
+pat0008
+pat0182
+pat0027
+pat0263
+pat0599
+pat0643
+pat0812
+pat0883
+pat0165
+pat0117
+pat0239
+