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GoodBaiBai88
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M3D-Seg

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multi-modal
3D medical segmentation
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  1. data_load_demo.py +22 -0
  2. data_process.py +175 -0
  3. dataset_info.json +383 -0
  4. dataset_info.txt +55 -0
data_load_demo.py ADDED
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1
+ import numpy as np
2
+ from scipy import sparse
3
+ import ast
4
+ import os
5
+ import json
6
+
7
+ uniseg_path = '/zhaobai/joint_data/UniSeg' # your path
8
+ dataset_code = '0012'
9
+ json_path = os.path.join('./', dataset_code, dataset_code + '.json')
10
+ with open(json_path, 'r') as f:
11
+ dataset_dict = json.load(f)
12
+
13
+ ct_file_path = os.path.join(uniseg_path, dataset_dict['training'][0]['image'])
14
+ gt_file_path = os.path.join(uniseg_path, dataset_dict['training'][0]['label'])
15
+
16
+ img_array = np.load(ct_file_path)[0]
17
+ print('img_array.shape ', img_array.shape)
18
+
19
+ allmatrix_sp= sparse.load_npz(gt_file_path)
20
+ gt_shape = ast.literal_eval(gt_file_path.split('.')[-2])
21
+ gt_array=allmatrix_sp.toarray().reshape(gt_shape)
22
+ print('gt_array.shape ', gt_array.shape)
data_process.py ADDED
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1
+ import os
2
+ import numpy as np
3
+ import multiprocessing
4
+ import argparse
5
+ from scipy import sparse
6
+ from sklearn.model_selection import train_test_split
7
+ import json
8
+
9
+ from monai.transforms import (
10
+ AddChanneld,
11
+ Compose,
12
+ LoadImaged,
13
+ Orientationd,
14
+ )
15
+
16
+ def set_parse():
17
+ # %% set up parser
18
+ parser = argparse.ArgumentParser()
19
+ parser.add_argument("-category", default=['liver', 'right kidney', 'spleen', 'pancreas', 'aorta', 'inferior vena cava', 'right adrenal gland', 'left adrenal gland', 'gallbladder', 'esophagus', 'stomach', 'duodenum', 'left kidney'], type=list)
20
+ parser.add_argument("-image_dir", type=str, required=True)
21
+ parser.add_argument("-label_dir", type=str, required=True)
22
+ parser.add_argument("-dataset_code", type=str, required=True)
23
+ parser.add_argument("-save_root", type=str, required=True)
24
+ parser.add_argument("-test_ratio", type=float, required=True)
25
+
26
+ args = parser.parse_args()
27
+ return args
28
+
29
+ args = set_parse()
30
+
31
+ # get ct&gt dir
32
+ image_list_all = [item for item in sorted(os.listdir(args.image_dir))]
33
+ label_list_all = [item for item in sorted(os.listdir(args.label_dir))]
34
+ assert len(image_list_all) == len(label_list_all)
35
+ print('dataset size ', len(image_list_all))
36
+
37
+ # build dataset
38
+ data_path_list_all = []
39
+ for idx in range(len(image_list_all)):
40
+ img_path = os.path.join(args.image_dir, image_list_all[idx])
41
+ label_path = os.path.join(args.label_dir, label_list_all[idx])
42
+ name = image_list_all[idx].split('.')[0]
43
+ info = (idx, name, img_path, label_path)
44
+ data_path_list_all.append(info)
45
+
46
+ img_loader = Compose(
47
+ [
48
+ LoadImaged(keys=['image', 'label']),
49
+ AddChanneld(keys=['image', 'label']),
50
+ Orientationd(keys=['image', 'label'], axcodes="RAS"),
51
+ ]
52
+ )
53
+
54
+ # save
55
+ save_path = os.path.join(args.save_root, args.dataset_code)
56
+ ct_save_path = os.path.join(save_path, 'ct')
57
+ gt_save_path = os.path.join(save_path, 'gt')
58
+ if not os.path.exists(ct_save_path):
59
+ os.makedirs(ct_save_path)
60
+ if not os.path.exists(gt_save_path):
61
+ os.makedirs(gt_save_path)
62
+
63
+ # exist file:
64
+ exist_file_list = os.listdir(ct_save_path)
65
+ print('exist_file_list ', exist_file_list)
66
+
67
+ def normalize(ct_narray):
68
+ ct_voxel_ndarray = ct_narray.copy()
69
+ ct_voxel_ndarray = ct_voxel_ndarray.flatten()
70
+ # for all data
71
+ thred = np.mean(ct_voxel_ndarray)
72
+ voxel_filtered = ct_voxel_ndarray[(ct_voxel_ndarray > thred)]
73
+ # for foreground data
74
+ upper_bound = np.percentile(voxel_filtered, 99.95)
75
+ lower_bound = np.percentile(voxel_filtered, 00.05)
76
+ mean = np.mean(voxel_filtered)
77
+ std = np.std(voxel_filtered)
78
+ ### transform ###
79
+ ct_narray = np.clip(ct_narray, lower_bound, upper_bound)
80
+ ct_narray = (ct_narray - mean) / max(std, 1e-8)
81
+ return ct_narray
82
+
83
+ def run(info):
84
+ idx, file_name, case_path, label_path = info
85
+ item = {}
86
+ if file_name + '.npy' in exist_file_list:
87
+ print(file_name + '.npy exist, skip')
88
+ return
89
+ print('process ', idx, '---' ,file_name)
90
+ # generate ct_voxel_ndarray
91
+ item_load = {
92
+ 'image' : case_path,
93
+ 'label' : label_path,
94
+ }
95
+ item_load = img_loader(item_load)
96
+ ct_voxel_ndarray = item_load['image']
97
+ gt_voxel_ndarray = item_load['label']
98
+
99
+ ct_shape = ct_voxel_ndarray.shape
100
+ item['image'] = ct_voxel_ndarray
101
+
102
+ # generate gt_voxel_ndarray
103
+ gt_voxel_ndarray = np.array(gt_voxel_ndarray).squeeze()
104
+ present_categories = np.unique(gt_voxel_ndarray)
105
+ gt_masks = []
106
+ for cls_idx in range(len(args.category)):
107
+ cls = cls_idx + 1
108
+ if cls not in present_categories:
109
+ gt_voxel_ndarray_category = np.zeros(ct_shape)
110
+ gt_masks.append(gt_voxel_ndarray_category)
111
+ print('case {} ==> zero category '.format(idx) + args.category[cls_idx])
112
+ print(gt_voxel_ndarray_category.shape)
113
+ else:
114
+ gt_voxel_ndarray_category = gt_voxel_ndarray.copy()
115
+ gt_voxel_ndarray_category[gt_voxel_ndarray != cls] = 0
116
+ gt_voxel_ndarray_category[gt_voxel_ndarray == cls] = 1
117
+ gt_masks.append(gt_voxel_ndarray_category)
118
+ gt_voxel_ndarray = np.stack(gt_masks, axis=0)
119
+
120
+ assert gt_voxel_ndarray.shape[0] == len(args.category), str(gt_voxel_ndarray.shape[0])
121
+ assert gt_voxel_ndarray.shape[1:] == ct_voxel_ndarray.shape[1:]
122
+ item['label'] = gt_voxel_ndarray.astype(np.int32)
123
+ print(idx, ' load done!')
124
+
125
+ #############################
126
+ item['image'] = normalize(item['image'])
127
+ print(idx, ' transform done')
128
+
129
+ ############################
130
+ print(file_name + ' ct gt <--> ', item['image'].shape, item['label'].shape)
131
+ np.save(os.path.join(ct_save_path, file_name + '.npy'), item['image'])
132
+ allmatrix_sp=sparse.csr_matrix(item['label'].reshape(item['label'].shape[0], -1))
133
+ sparse.save_npz(os.path.join(gt_save_path, file_name + '.' + str(item['label'].shape)), allmatrix_sp)
134
+ print(file_name + ' save done!')
135
+
136
+ def generate_dataset_json(root_dir, output_file, test_ratio=0.2):
137
+ ct_dir = os.path.join(root_dir, 'ct')
138
+ gt_dir = os.path.join(root_dir, 'gt')
139
+ ct_paths = sorted([os.path.join(ct_dir, f) for f in sorted(os.listdir(ct_dir))])
140
+ gt_paths = sorted([os.path.join(gt_dir, f) for f in sorted(os.listdir(gt_dir))])
141
+
142
+ data = list(zip(ct_paths, gt_paths))
143
+ train_data, val_data = train_test_split(data, test_size=test_ratio)
144
+ labels = {}
145
+ labels['0'] = 'background'
146
+ for idx in range(len(args.category)):
147
+ label_name = args.category[idx]
148
+ label_id = idx + 1
149
+ labels[str(label_id)] = label_name
150
+ dataset = {
151
+ 'name': f'{args.dataset_code} Dataset',
152
+ 'description': f'{args.dataset_code} Dataset',
153
+ 'tensorImageSize': '4D',
154
+ 'modality': {
155
+ '0': 'CT',
156
+ },
157
+ 'labels': labels,
158
+ 'numTraining': len(train_data),
159
+ 'numTest': len(val_data),
160
+ 'training': [{'image': ct_path, 'label': gt_path} for ct_path, gt_path in train_data],
161
+ 'validation': [{'image': ct_path, 'label': gt_path} for ct_path, gt_path in val_data]
162
+ }
163
+ with open(output_file, 'w') as f:
164
+ print(f'{output_file} dump')
165
+ json.dump(dataset, f, indent=2)
166
+
167
+ if __name__ == "__main__":
168
+ with multiprocessing.Pool(processes=10) as pool:
169
+ pool.map(run, data_path_list_all)
170
+ print('Process Finished!')
171
+
172
+ generate_dataset_json(root_dir=save_path,
173
+ output_file=os.path.join(save_path, f'{args.dataset_code}.json'),
174
+ test_ratio=args.test_ratio)
175
+ print('Json Split Done!')
dataset_info.json ADDED
@@ -0,0 +1,383 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "0000": [
3
+ "liver"
4
+ ],
5
+ "0001": [
6
+ "A_Carotid_L",
7
+ "A_Carotid_R",
8
+ "Arytenoid",
9
+ "Bone_Mandible",
10
+ "Brainstem",
11
+ "BuccalMucosa",
12
+ "Cavity_Oral",
13
+ "Cochlea_L",
14
+ "Cochlea_R",
15
+ "Cricopharyngeus",
16
+ "Esophagus_S",
17
+ "Eye_AL",
18
+ "Eye_AR",
19
+ "Eye_PL",
20
+ "Eye_PR",
21
+ "Glnd_Lacrimal_L",
22
+ "Glnd_Lacrimal_R",
23
+ "Glnd_Submand_L",
24
+ "Glnd_Submand_R",
25
+ "Glnd_Thyroid",
26
+ "Glottis",
27
+ "Larynx_SG",
28
+ "Lips",
29
+ "OpticChiasm",
30
+ "OpticNrv_L",
31
+ "OpticNrv_R",
32
+ "Parotid_L",
33
+ "Parotid_R",
34
+ "Pituitary",
35
+ "SpinalCord"
36
+ ],
37
+ "0002": [
38
+ "spleen",
39
+ "right kidney",
40
+ "left kidney",
41
+ "gall bladder",
42
+ "esophagus",
43
+ "liver",
44
+ "stomach",
45
+ "arota",
46
+ "postcava",
47
+ "pancreas",
48
+ "right adrenal gland",
49
+ "left adrenal gland",
50
+ "duodenum",
51
+ "bladder",
52
+ "prostate/uterus"
53
+ ],
54
+ "0003": [
55
+ "liver",
56
+ "kidney",
57
+ "spleen",
58
+ "pancreas"
59
+ ],
60
+ "0004": [
61
+ "kidney",
62
+ "kidney tumor",
63
+ "kidney cyst"
64
+ ],
65
+ "0005": [
66
+ "Renal vein",
67
+ "Kidney",
68
+ "Renal artery",
69
+ "Tumor"
70
+ ],
71
+ "0006": [
72
+ "kidney",
73
+ "kidney tumor"
74
+ ],
75
+ "0007": [
76
+ "spleen",
77
+ "right kidney",
78
+ "left kidney",
79
+ "gallbladder",
80
+ "esophagus",
81
+ "liver",
82
+ "stomach",
83
+ "aorta",
84
+ "inferior vena cava",
85
+ "portal vein and splenic vein",
86
+ "pancreas",
87
+ "right adrenal gland",
88
+ "left adrenal gland"
89
+ ],
90
+ "0008": [
91
+ "pancreas"
92
+ ],
93
+ "0009": [
94
+ "Stones",
95
+ "artery",
96
+ "biliarysystem",
97
+ "bladder",
98
+ "bone",
99
+ "colon",
100
+ "gallbladder",
101
+ "heart",
102
+ "kidneys",
103
+ "leftkidney",
104
+ "leftlung",
105
+ "leftsurrenalgland",
106
+ "leftsurretumor",
107
+ "liver",
108
+ "livercyst",
109
+ "liverkyst",
110
+ "liverkyste",
111
+ "livertumor",
112
+ "livertumor01",
113
+ "livertumor02",
114
+ "livertumor03",
115
+ "livertumor04",
116
+ "livertumor05",
117
+ "livertumor06",
118
+ "livertumor07",
119
+ "livertumor1",
120
+ "livertumor2",
121
+ "livertumors",
122
+ "lungs",
123
+ "metal",
124
+ "metastasectomie",
125
+ "pancreas",
126
+ "portalvein",
127
+ "portalvein1",
128
+ "rightkidney",
129
+ "rightlung",
130
+ "rightsurrenalgland",
131
+ "rightsurretumor",
132
+ "skin",
133
+ "smallintestin",
134
+ "spleen",
135
+ "stomach",
136
+ "surrenalgland",
137
+ "tumor",
138
+ "uterus",
139
+ "venacava",
140
+ "venoussystem"
141
+ ],
142
+ "0010": [
143
+ "liver",
144
+ "right kidney",
145
+ "spleen",
146
+ "pancreas",
147
+ "aorta",
148
+ "inferior vena cava",
149
+ "right adrenal gland",
150
+ "left adrenal gland",
151
+ "gallbladder",
152
+ "esophagus",
153
+ "stomach",
154
+ "duodenum",
155
+ "left kidney"
156
+ ],
157
+ "0011": [
158
+ "adrenal_gland_left",
159
+ "adrenal_gland_right",
160
+ "aorta",
161
+ "autochthon_left",
162
+ "autochthon_right",
163
+ "brain",
164
+ "clavicula_left",
165
+ "clavicula_right",
166
+ "colon",
167
+ "duodenum",
168
+ "esophagus",
169
+ "face",
170
+ "femur_left",
171
+ "femur_right",
172
+ "gallbladder",
173
+ "gluteus_maximus_left",
174
+ "gluteus_maximus_right",
175
+ "gluteus_medius_left",
176
+ "gluteus_medius_right",
177
+ "gluteus_minimus_left",
178
+ "gluteus_minimus_right",
179
+ "heart_atrium_left",
180
+ "heart_atrium_right",
181
+ "heart_myocardium",
182
+ "heart_ventricle_left",
183
+ "heart_ventricle_right",
184
+ "hip_left",
185
+ "hip_right",
186
+ "humerus_left",
187
+ "humerus_right",
188
+ "iliac_artery_left",
189
+ "iliac_artery_right",
190
+ "iliac_vena_left",
191
+ "iliac_vena_right",
192
+ "iliopsoas_left",
193
+ "iliopsoas_right",
194
+ "inferior_vena_cava",
195
+ "kidney_left",
196
+ "kidney_right",
197
+ "liver",
198
+ "lung_lower_lobe_left",
199
+ "lung_lower_lobe_right",
200
+ "lung_middle_lobe_right",
201
+ "lung_upper_lobe_left",
202
+ "lung_upper_lobe_right",
203
+ "pancreas",
204
+ "portal_vein_and_splenic_vein",
205
+ "pulmonary_artery",
206
+ "rib_left_1",
207
+ "rib_left_10",
208
+ "rib_left_11",
209
+ "rib_left_12",
210
+ "rib_left_2",
211
+ "rib_left_3",
212
+ "rib_left_4",
213
+ "rib_left_5",
214
+ "rib_left_6",
215
+ "rib_left_7",
216
+ "rib_left_8",
217
+ "rib_left_9",
218
+ "rib_right_1",
219
+ "rib_right_10",
220
+ "rib_right_11",
221
+ "rib_right_12",
222
+ "rib_right_2",
223
+ "rib_right_3",
224
+ "rib_right_4",
225
+ "rib_right_5",
226
+ "rib_right_6",
227
+ "rib_right_7",
228
+ "rib_right_8",
229
+ "rib_right_9",
230
+ "sacrum",
231
+ "scapula_left",
232
+ "scapula_right",
233
+ "small_bowel",
234
+ "spleen",
235
+ "stomach",
236
+ "trachea",
237
+ "urinary_bladder",
238
+ "vertebrae_C1",
239
+ "vertebrae_C2",
240
+ "vertebrae_C3",
241
+ "vertebrae_C4",
242
+ "vertebrae_C5",
243
+ "vertebrae_C6",
244
+ "vertebrae_C7",
245
+ "vertebrae_L1",
246
+ "vertebrae_L2",
247
+ "vertebrae_L3",
248
+ "vertebrae_L4",
249
+ "vertebrae_L5",
250
+ "vertebrae_T1",
251
+ "vertebrae_T10",
252
+ "vertebrae_T11",
253
+ "vertebrae_T12",
254
+ "vertebrae_T2",
255
+ "vertebrae_T3",
256
+ "vertebrae_T4",
257
+ "vertebrae_T5",
258
+ "vertebrae_T6",
259
+ "vertebrae_T7",
260
+ "vertebrae_T8",
261
+ "vertebrae_T9"
262
+ ],
263
+ "0012": [
264
+ "Liver",
265
+ "Bladder",
266
+ "Lungs",
267
+ "Kidneys",
268
+ "Bone",
269
+ "Brain"
270
+ ],
271
+ "0013": [
272
+ "Liver",
273
+ "Spleen",
274
+ "Kidney (L)",
275
+ "Kidney (R)",
276
+ "Stomach",
277
+ "Gallbladder",
278
+ "Esophagus",
279
+ "Pancreas",
280
+ "Duodenum",
281
+ "Colon",
282
+ "Intestine",
283
+ "Adrenal",
284
+ "Rectum",
285
+ "Bladder",
286
+ "Head of femur (L)",
287
+ "Head of femur (R)"
288
+ ],
289
+ "0014": [
290
+ "cervical spine C1",
291
+ "cervical spine C2",
292
+ "cervical spine C3",
293
+ "cervical spine C4",
294
+ "cervical spine C5",
295
+ "cervical spine C6",
296
+ "cervical spine C7",
297
+ "thoracic spine T1",
298
+ "thoracic spine T2",
299
+ "thoracic spine T3",
300
+ "thoracic spine T4",
301
+ "thoracic spine T5",
302
+ "thoracic spine T6",
303
+ "thoracic spine T7",
304
+ "thoracic spine T8",
305
+ "thoracic spine T9",
306
+ "thoracic spine T10",
307
+ "thoracic spine T11",
308
+ "thoracic spine T12",
309
+ "lumbar spine L1",
310
+ "lumbar spine L2",
311
+ "lumbar spine L3",
312
+ "lumbar spine L4",
313
+ "lumbar spine L5",
314
+ "lumbar spine L6",
315
+ "sacrum",
316
+ "cocygis",
317
+ "additional 13th thoracic vertebra, T13"
318
+ ],
319
+ "0015": [
320
+ "cervical spine C1",
321
+ "cervical spine C2",
322
+ "cervical spine C3",
323
+ "cervical spine C4",
324
+ "cervical spine C5",
325
+ "cervical spine C6",
326
+ "cervical spine C7",
327
+ "thoracic spine T1",
328
+ "thoracic spine T2",
329
+ "thoracic spine T3",
330
+ "thoracic spine T4",
331
+ "thoracic spine T5",
332
+ "thoracic spine T6",
333
+ "thoracic spine T7",
334
+ "thoracic spine T8",
335
+ "thoracic spine T9",
336
+ "thoracic spine T10",
337
+ "thoracic spine T11",
338
+ "thoracic spine T12",
339
+ "lumbar spine L1",
340
+ "lumbar spine L2",
341
+ "lumbar spine L3",
342
+ "lumbar spine L4",
343
+ "lumbar spine L5",
344
+ "lumbar spine L6",
345
+ "sacrum",
346
+ "cocygis",
347
+ "additional 13th thoracic vertebra, T13"
348
+ ],
349
+ "0016": [
350
+ "liver"
351
+ ],
352
+ "0017": [
353
+ "kidney",
354
+ "pancreas",
355
+ "pancreatic-lesion"
356
+ ],
357
+ "0018": [
358
+ "colon cancer"
359
+ ],
360
+ "0019": [
361
+ "hepatic vessels",
362
+ "tumour"
363
+ ],
364
+ "0020": [
365
+ "liver",
366
+ "tumour"
367
+ ],
368
+ "0021": [
369
+ "lung tumours"
370
+ ],
371
+ "0022": [
372
+ "pancreas",
373
+ "tumour"
374
+ ],
375
+ "0023": [
376
+ "spleen"
377
+ ],
378
+ "0024": [
379
+ "left lung",
380
+ "right lung",
381
+ "trachea"
382
+ ]
383
+ }
dataset_info.txt ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dataset_name = {
2
+ '0000': 'CHAOS',
3
+ '0001': 'HaN-Seg',
4
+ '0002': 'AMOS22',
5
+ '0003': 'AbdomenCT-1k',
6
+ '0004': 'KiTS23',
7
+ '0005': 'KiPA22',
8
+ '0006': 'KiTS19',
9
+ '0007': 'BTCV',
10
+ '0008': 'Pancreas-CT',
11
+ '0009': '3D-IRCADb',
12
+ '0010': 'FLARE22',
13
+ '0011': 'TotalSegmentator',
14
+ '0012': 'CT-ORG',
15
+ '0013': 'WORD',
16
+ '0014': 'VerSe19',
17
+ '0015': 'VerSe20',
18
+ '0016': 'SLIVER07',
19
+ '0017': 'QUBIQ',
20
+ '0018': 'MSD-colon',
21
+ '0019': 'MSD-hepatic_vessel',
22
+ '0020': 'MSD-liver',
23
+ '0021': 'MSD-lung',
24
+ '0022': 'MSD-pancreas',
25
+ '0023': 'MSD-spleen',
26
+ '0024': 'LUNA16',
27
+ }
28
+
29
+ dataset = {
30
+ '0000': ['liver'],
31
+ '0001': ['A_Carotid_L','A_Carotid_R','Arytenoid','Bone_Mandible','Brainstem','BuccalMucosa','Cavity_Oral','Cochlea_L','Cochlea_R','Cricopharyngeus','Esophagus_S','Eye_AL','Eye_AR','Eye_PL','Eye_PR','Glnd_Lacrimal_L','Glnd_Lacrimal_R','Glnd_Submand_L','Glnd_Submand_R','Glnd_Thyroid','Glottis','Larynx_SG','Lips','OpticChiasm','OpticNrv_L','OpticNrv_R','Parotid_L','Parotid_R','Pituitary','SpinalCord'],
32
+ '0002': ["spleen", "right kidney", "left kidney", "gall bladder", "esophagus", "liver", "stomach", "arota", "postcava", "pancreas", "right adrenal gland", "left adrenal gland", "duodenum", "bladder", "prostate/uterus"],
33
+ '0003': ['liver', 'kidney', 'spleen', 'pancreas'],
34
+ '0004': ['kidney', 'kidney tumor', 'kidney cyst'],
35
+ '0005': ['Renal vein', 'Kidney', 'Renal artery', 'Tumor'],
36
+ '0006': ['kidney', 'kidney tumor'],
37
+ '0007': ['spleen','right kidney','left kidney','gallbladder','esophagus','liver','stomach','aorta','inferior vena cava','portal vein and splenic vein','pancreas','right adrenal gland','left adrenal gland'],
38
+ '0008': ['pancreas'],
39
+ '0009': ['Stones', 'artery', 'biliarysystem', 'bladder', 'bone', 'colon', 'gallbladder', 'heart', 'kidneys', 'leftkidney', 'leftlung', 'leftsurrenalgland', 'leftsurretumor', 'liver', 'livercyst', 'liverkyst', 'liverkyste', 'livertumor', 'livertumor01', 'livertumor02', 'livertumor03', 'livertumor04', 'livertumor05', 'livertumor06', 'livertumor07', 'livertumor1', 'livertumor2', 'livertumors', 'lungs', 'metal', 'metastasectomie', 'pancreas', 'portalvein', 'portalvein1', 'rightkidney', 'rightlung', 'rightsurrenalgland', 'rightsurretumor', 'skin', 'smallintestin', 'spleen', 'stomach', 'surrenalgland', 'tumor', 'uterus', 'venacava', 'venoussystem'],
40
+ '0010': ['liver', 'right kidney', 'spleen', 'pancreas', 'aorta', 'inferior vena cava', 'right adrenal gland', 'left adrenal gland', 'gallbladder', 'esophagus', 'stomach', 'duodenum', 'left kidney'],
41
+ '0011': ['adrenal_gland_left', 'adrenal_gland_right', 'aorta', 'autochthon_left', 'autochthon_right', 'brain', 'clavicula_left', 'clavicula_right', 'colon', 'duodenum', 'esophagus', 'face', 'femur_left', 'femur_right', 'gallbladder', 'gluteus_maximus_left', 'gluteus_maximus_right', 'gluteus_medius_left', 'gluteus_medius_right', 'gluteus_minimus_left', 'gluteus_minimus_right', 'heart_atrium_left', 'heart_atrium_right', 'heart_myocardium', 'heart_ventricle_left', 'heart_ventricle_right', 'hip_left', 'hip_right', 'humerus_left', 'humerus_right', 'iliac_artery_left', 'iliac_artery_right', 'iliac_vena_left', 'iliac_vena_right', 'iliopsoas_left', 'iliopsoas_right', 'inferior_vena_cava', 'kidney_left', 'kidney_right', 'liver', 'lung_lower_lobe_left', 'lung_lower_lobe_right', 'lung_middle_lobe_right', 'lung_upper_lobe_left', 'lung_upper_lobe_right', 'pancreas', 'portal_vein_and_splenic_vein', 'pulmonary_artery', 'rib_left_1', 'rib_left_10', 'rib_left_11', 'rib_left_12', 'rib_left_2', 'rib_left_3', 'rib_left_4', 'rib_left_5', 'rib_left_6', 'rib_left_7', 'rib_left_8', 'rib_left_9', 'rib_right_1', 'rib_right_10', 'rib_right_11', 'rib_right_12', 'rib_right_2', 'rib_right_3', 'rib_right_4', 'rib_right_5', 'rib_right_6', 'rib_right_7', 'rib_right_8', 'rib_right_9', 'sacrum', 'scapula_left', 'scapula_right', 'small_bowel', 'spleen', 'stomach', 'trachea', 'urinary_bladder', 'vertebrae_C1', 'vertebrae_C2', 'vertebrae_C3', 'vertebrae_C4', 'vertebrae_C5', 'vertebrae_C6', 'vertebrae_C7', 'vertebrae_L1', 'vertebrae_L2', 'vertebrae_L3', 'vertebrae_L4', 'vertebrae_L5', 'vertebrae_T1', 'vertebrae_T10', 'vertebrae_T11', 'vertebrae_T12', 'vertebrae_T2', 'vertebrae_T3', 'vertebrae_T4', 'vertebrae_T5', 'vertebrae_T6', 'vertebrae_T7', 'vertebrae_T8', 'vertebrae_T9'],
42
+ '0012': ['Liver','Bladder','Lungs','Kidneys','Bone','Brain'],
43
+ '0013': ['Liver','Spleen','Kidney (L)','Kidney (R)','Stomach','Gallbladder','Esophagus','Pancreas','Duodenum','Colon','Intestine','Adrenal','Rectum','Bladder','Head of femur (L)','Head of femur (R)'],
44
+ '0014': ['cervical spine C1', 'cervical spine C2', 'cervical spine C3', 'cervical spine C4', 'cervical spine C5', 'cervical spine C6', 'cervical spine C7', 'thoracic spine T1', 'thoracic spine T2', 'thoracic spine T3', 'thoracic spine T4', 'thoracic spine T5', 'thoracic spine T6', 'thoracic spine T7', 'thoracic spine T8', 'thoracic spine T9', 'thoracic spine T10', 'thoracic spine T11', 'thoracic spine T12', 'lumbar spine L1', 'lumbar spine L1', 'lumbar spine L3', 'lumbar spine L4', 'lumbar spine L5', 'lumbar spine L6', 'sacrum','cocygis','additional 13th thoracic vertebra, T13',],
45
+ '0015': ['cervical spine C1', 'cervical spine C2', 'cervical spine C3', 'cervical spine C4', 'cervical spine C5', 'cervical spine C6', 'cervical spine C7', 'thoracic spine T1', 'thoracic spine T2', 'thoracic spine T3', 'thoracic spine T4', 'thoracic spine T5', 'thoracic spine T6', 'thoracic spine T7', 'thoracic spine T8', 'thoracic spine T9', 'thoracic spine T10', 'thoracic spine T11', 'thoracic spine T12', 'lumbar spine L1', 'lumbar spine L1', 'lumbar spine L3', 'lumbar spine L4', 'lumbar spine L5', 'lumbar spine L6', 'sacrum','cocygis','additional 13th thoracic vertebra, T13',],
46
+ '0016': ['liver'],
47
+ '0017': ['kidney', 'pancreas', 'pancreatic-lesion'],
48
+ '0018': ['colon cancer'],
49
+ '0019': ['hepatic vessels', 'tumour'],
50
+ '0020': ['liver', 'tumour'],
51
+ '0021': ['lung tumours'],
52
+ '0022': ['pancreas', 'tumour'],
53
+ '0023': ['spleen'],
54
+ '0024': ['left lung', 'right lung', 'trachea'],
55
+ }