-
Notifications
You must be signed in to change notification settings - Fork 0
/
s1_preprocess_luna16.py
768 lines (674 loc) · 35.7 KB
/
s1_preprocess_luna16.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
import settings
import helpers
import SimpleITK # conda install -c https://conda.anaconda.org/simpleitk SimpleITK
import numpy
import pandas
import ntpath
import cv2 # conda install -c https://conda.anaconda.org/menpo opencv3
import shutil
import random
import math
import multiprocessing
from bs4 import BeautifulSoup # conda install beautifulsoup4, conda install lxml
import os
import glob
import pdb
random.seed(1321)
numpy.random.seed(1321)
def find_mhd_file(patient_id):
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if patient_id in src_path:
return src_path
return None
# output: patient_pos_lidc.csv, patient_neg_lidc.csv
def load_lidc_xml(xml_path, agreement_threshold=0, only_patient=None, save_nodules=False):
pos_lines = []
neg_lines = []
extended_lines = []
with open(xml_path, 'r') as xml_file:
markup = xml_file.read()
xml = BeautifulSoup(markup, features="xml")
if xml.LidcReadMessage is None:
return None, None, None
patient_id = xml.LidcReadMessage.ResponseHeader.SeriesInstanceUid.text
if only_patient is not None:
if only_patient != patient_id:
return None, None, None
src_path = find_mhd_file(patient_id)
if src_path is None:
return None, None, None
print(patient_id)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
rescale = spacing / settings.TARGET_VOXEL_MM
reading_sessions = xml.LidcReadMessage.find_all("readingSession")
for reading_session in reading_sessions:
# print("Sesion")
nodules = reading_session.find_all("unblindedReadNodule")
for nodule in nodules:
nodule_id = nodule.noduleID.text
# print(" ", nodule.noduleID)
rois = nodule.find_all("roi")
x_min = y_min = z_min = 999999
x_max = y_max = z_max = -999999
if len(rois) < 2:
continue
for roi in rois:
z_pos = float(roi.imageZposition.text)
z_min = min(z_min, z_pos)
z_max = max(z_max, z_pos)
edge_maps = roi.find_all("edgeMap")
for edge_map in edge_maps:
x = int(edge_map.xCoord.text)
y = int(edge_map.yCoord.text)
x_min = min(x_min, x)
y_min = min(y_min, y)
x_max = max(x_max, x)
y_max = max(y_max, y)
if x_max == x_min:
continue
if y_max == y_min:
continue
x_diameter = x_max - x_min
x_center = x_min + x_diameter / 2
y_diameter = y_max - y_min
y_center = y_min + y_diameter / 2
z_diameter = z_max - z_min
z_center = z_min + z_diameter / 2
z_center -= origin[2]
z_center /= spacing[2]
x_center_perc = round(x_center / img_array.shape[2], 4)
y_center_perc = round(y_center / img_array.shape[1], 4)
z_center_perc = round(z_center / img_array.shape[0], 4)
diameter = max(x_diameter , y_diameter)
diameter_perc = round(max(x_diameter / img_array.shape[2], y_diameter / img_array.shape[1]), 4)
if nodule.characteristics is None:
print("!!!!Nodule:", nodule_id, " has no charecteristics")
continue
if nodule.characteristics.malignancy is None:
print("!!!!Nodule:", nodule_id, " has no malignacy")
continue
malignacy = nodule.characteristics.malignancy.text
sphericiy = nodule.characteristics.sphericity.text
margin = nodule.characteristics.margin.text
spiculation = nodule.characteristics.spiculation.text
texture = nodule.characteristics.texture.text
calcification = nodule.characteristics.calcification.text
internal_structure = nodule.characteristics.internalStructure.text
lobulation = nodule.characteristics.lobulation.text
subtlety = nodule.characteristics.subtlety.text
line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy]
extended_line = [patient_id, nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy, sphericiy, margin, spiculation, texture, calcification, internal_structure, lobulation, subtlety ]
pos_lines.append(line)
extended_lines.append(extended_line)
nonNodules = reading_session.find_all("nonNodule")
for nonNodule in nonNodules:
z_center = float(nonNodule.imageZposition.text)
z_center -= origin[2]
z_center /= spacing[2]
x_center = int(nonNodule.locus.xCoord.text)
y_center = int(nonNodule.locus.yCoord.text)
nodule_id = nonNodule.nonNoduleID.text
x_center_perc = round(x_center / img_array.shape[2], 4)
y_center_perc = round(y_center / img_array.shape[1], 4)
z_center_perc = round(z_center / img_array.shape[0], 4)
diameter_perc = round(max(6 / img_array.shape[2], 6 / img_array.shape[1]), 4)
# print("Non nodule!", z_center)
line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, 0]
neg_lines.append(line)
if agreement_threshold > 1:
filtered_lines = []
for pos_line1 in pos_lines:
id1 = pos_line1[0]
x1 = pos_line1[1]
y1 = pos_line1[2]
z1 = pos_line1[3]
d1 = pos_line1[4]
overlaps = 0
for pos_line2 in pos_lines:
id2 = pos_line2[0]
if id1 == id2:
continue
x2 = pos_line2[1]
y2 = pos_line2[2]
z2 = pos_line2[3]
d2 = pos_line1[4]
dist = math.sqrt(math.pow(x1 - x2, 2) + math.pow(y1 - y2, 2) + math.pow(z1 - z2, 2))
if dist < d1 or dist < d2:
overlaps += 1
if overlaps >= agreement_threshold:
filtered_lines.append(pos_line1)
# else:
# print("Too few overlaps")
pos_lines = filtered_lines
df_annos = pandas.DataFrame(pos_lines, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos_lidc.csv", index=False)
df_neg_annos = pandas.DataFrame(neg_lines, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_neg_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_neg_lidc.csv", index=False)
# return [patient_id, spacing[0], spacing[1], spacing[2]]
return pos_lines, neg_lines, extended_lines
def normalize(image):
MIN_BOUND = -1000.0
MAX_BOUND = 400.0
image = (image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND)
image[image > 1] = 1.
image[image < 0] = 0.
return image
# output: patient/imageslices.png (for each of 888 luna scans)
def process_image(src_path):
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_id)
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
img_array = helpers.rescale_patient_images(img_array, spacing, settings.TARGET_VOXEL_MM)
img_list = []
for i in range(img_array.shape[0]):
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png", img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png", mask * 255)
# output: patient_annos_pos.csv (for each of 888 luna scans)
def process_pos_annotations_patient(src_path, patient_id):
df_node = pandas.read_csv("resources/luna16_annotations/annotations.csv")
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
dst_dir = dst_dir + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
df_patient = df_node[df_node["seriesuid"] == patient_id]
print("Annos: ", len(df_patient))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing /settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
pos_annos = []
df_patient = df_node[df_node["seriesuid"] == patient_id]
anno_index = 0
for index, annotation in df_patient.iterrows():
node_x = annotation["coordX"]
if flip_direction_x:
node_x *= -1
node_y = annotation["coordY"]
if flip_direction_y:
node_y *= -1
node_z = annotation["coordZ"]
diam_mm = annotation["diameter_mm"]
print("Node org (x,y,z,diam): ", (round(node_x, 2), round(node_y, 2), round(node_z, 2), round(diam_mm, 2)))
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
# center_int = numpy.rint((center_float - origin) )
print("Node tra (x,y,z,diam): ", (center_int[0], center_int[1], center_int[2]))
# center_int_rescaled = numpy.rint(((center_float-origin) / spacing) * rescale)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
diameter_pixels = diam_mm / settings.TARGET_VOXEL_MM
diameter_percent = diameter_pixels / float(patient_imgs.shape[1])
pos_annos.append([anno_index, round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(diameter_percent, 4), 1])
anno_index += 1
df_annos = pandas.DataFrame(pos_annos, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos.csv", index=False)
return [patient_id, spacing[0], spacing[1], spacing[2]]
# output: patient_annos_excluded.csv
def process_excluded_annotations_patient(src_path, patient_id):
df_node = pandas.read_csv("resources/luna16_annotations/annotations_excluded.csv")
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
dst_dir = dst_dir + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
# pos_annos_df = pandas.read_csv(TRAIN_DIR + "metadata/" + patient_id + "_annos_pos_lidc.csv")
pos_annos_df = pandas.read_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos.csv")
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
dmm = pos_annos_manual["dmm"] # check
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
df_patient = df_node[df_node["seriesuid"] == patient_id]
print("Annos: ", len(df_patient))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
neg_annos = []
df_patient = df_node[df_node["seriesuid"] == patient_id]
anno_index = 0
for index, annotation in df_patient.iterrows():
node_x = annotation["coordX"]
if flip_direction_x:
node_x *= -1
node_y = annotation["coordY"]
if flip_direction_y:
node_y *= -1
node_z = annotation["coordZ"]
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
# print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
diameter_pixels = 6 / settings.TARGET_VOXEL_MM
diameter_percent = diameter_pixels / float(patient_imgs.shape[1])
ok = True
for index, row in pos_annos_df.iterrows():
pos_coord_x = row["coord_x"] * patient_imgs.shape[2]
pos_coord_y = row["coord_y"] * patient_imgs.shape[1]
pos_coord_z = row["coord_z"] * patient_imgs.shape[0]
diameter = row["diameter"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 64): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if pos_annos_manual is not None and ok:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * patient_imgs.shape[2]
pos_coord_y = row["y"] * patient_imgs.shape[1]
pos_coord_z = row["z"] * patient_imgs.shape[0]
diameter = row["d"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if not ok:
continue
neg_annos.append([anno_index, round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(diameter_percent, 4), 1])
anno_index += 1
df_annos = pandas.DataFrame(neg_annos, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_excluded.csv", index=False)
return [patient_id, spacing[0], spacing[1], spacing[2]]
# output: patient_candidates_luna.csv
def process_luna_candidates_patient(src_path, patient_id):
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
img_dir = dst_dir + patient_id + "/"
#pdb.set_trace()
df_pos_annos = pandas.read_csv(dst_dir + patient_id + "_annos_pos_lidc.csv")
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
#pdb.set_trace()
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
print("Pos annos: ", len(df_pos_annos))
num_z, height, width = img_array.shape # height X width constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
src_df = pandas.read_csv("resources/luna16_annotations/" + "candidates_V2.csv")
src_df = src_df[src_df["seriesuid"] == patient_id]
src_df = src_df[src_df["class"] == 0]
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
candidate_list = []
for df_index, candiate_row in src_df.iterrows():
node_x = candiate_row["coordX"]
if flip_direction_x:
node_x *= -1
node_y = candiate_row["coordY"]
if flip_direction_y:
node_y *= -1
node_z = candiate_row["coordZ"]
candidate_diameter = 6
# print("Node org (x,y,z,diam): ", (round(node_x, 2), round(node_y, 2), round(node_z, 2), round(candidate_diameter, 2)))
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
# center_int = numpy.rint((center_float - origin) )
# print("Node tra (x,y,z,diam): ", (center_int[0], center_int[1], center_int[2]))
# center_int_rescaled = numpy.rint(((center_float-origin) / spacing) * rescale)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
# print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
coord_x = center_float_rescaled[0]
coord_y = center_float_rescaled[1]
coord_z = center_float_rescaled[2]
ok = True
for index, row in df_pos_annos.iterrows():
pos_coord_x = row["coord_x"] * patient_imgs.shape[2]
pos_coord_y = row["coord_y"] * patient_imgs.shape[1]
pos_coord_z = row["coord_z"] * patient_imgs.shape[0]
diameter = row["diameter"] * patient_imgs.shape[2]
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 64): # make sure we have a big margin
ok = False
print("################### Too close", (coord_x, coord_y, coord_z))
break
if pos_annos_manual is not None and ok:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * patient_imgs.shape[2]
pos_coord_y = row["y"] * patient_imgs.shape[1]
pos_coord_z = row["z"] * patient_imgs.shape[0]
diameter = row["d"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if not ok:
continue
candidate_list.append([len(candidate_list), round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(candidate_diameter / patient_imgs.shape[0], 4), 0])
df_candidates = pandas.DataFrame(candidate_list, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_candidates.to_csv(dst_dir + patient_id + "_candidates_luna.csv", index=False)
# output: patient_candidates_edge.csv
def process_auto_candidates_patient(src_path, patient_id, sample_count=1000, candidate_type="white"):
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "/_labels/"
img_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
df_pos_annos = pandas.read_csv(dst_dir + patient_id + "_annos_pos_lidc.csv")
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
print("Pos annos: ", len(df_pos_annos))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
if candidate_type == "white":
wildcard = "*_c.png"
else:
wildcard = "*_m.png"
src_files = glob.glob(img_dir + wildcard)
src_files.sort()
src_candidate_maps = [cv2.imread(src_file, cv2.IMREAD_GRAYSCALE) for src_file in src_files]
candidate_list = []
tries = 0
while len(candidate_list) < sample_count and tries < 10000:
tries += 1
coord_z = int(numpy.random.normal(len(src_files) / 2, len(src_files) / 6))
coord_z = max(coord_z, 0)
coord_z = min(coord_z, len(src_files) - 1)
candidate_map = src_candidate_maps[coord_z]
if candidate_type == "edge":
candidate_map = cv2.Canny(candidate_map.copy(), 100, 200)
non_zero_indices = numpy.nonzero(candidate_map)
if len(non_zero_indices[0]) == 0:
continue
nonzero_index = random.randint(0, len(non_zero_indices[0]) - 1)
coord_y = non_zero_indices[0][nonzero_index]
coord_x = non_zero_indices[1][nonzero_index]
ok = True
candidate_diameter = 6
for index, row in df_pos_annos.iterrows():
pos_coord_x = row["coord_x"] * src_candidate_maps[0].shape[1]
pos_coord_y = row["coord_y"] * src_candidate_maps[0].shape[0]
pos_coord_z = row["coord_z"] * len(src_files)
diameter = row["diameter"] * src_candidate_maps[0].shape[1]
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 48): # make sure we have a big margin
ok = False
print("# Too close", (coord_x, coord_y, coord_z))
break
if pos_annos_manual is not None:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * src_candidate_maps[0].shape[1]
pos_coord_y = row["y"] * src_candidate_maps[0].shape[0]
pos_coord_z = row["z"] * len(src_files)
diameter = row["d"] * src_candidate_maps[0].shape[1]
# print((pos_coord_x, pos_coord_y, pos_coord_z))
# print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("#Too close", (coord_x, coord_y, coord_z))
break
if not ok:
continue
perc_x = round(coord_x / src_candidate_maps[coord_z].shape[1], 4)
perc_y = round(coord_y / src_candidate_maps[coord_z].shape[0], 4)
perc_z = round(coord_z / len(src_files), 4)
candidate_list.append([len(candidate_list), perc_x, perc_y, perc_z, round(candidate_diameter / src_candidate_maps[coord_z].shape[1], 4), 0])
if tries > 9999:
print("****** WARING!! TOO MANY TRIES ************************************")
df_candidates = pandas.DataFrame(candidate_list, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_candidates.to_csv(dst_dir + patient_id + "_candidates_" + candidate_type + ".csv", index=False)
# 1st
def process_images(delete_existing=False, only_process_patient=None):
if delete_existing and os.path.exists(settings.LUNA16_EXTRACTED_IMAGE_DIR):
print("Removing old stuff..")
if os.path.exists(settings.LUNA16_EXTRACTED_IMAGE_DIR):
shutil.rmtree(settings.LUNA16_EXTRACTED_IMAGE_DIR)
if not os.path.exists(settings.LUNA16_EXTRACTED_IMAGE_DIR):
os.mkdir(settings.LUNA16_EXTRACTED_IMAGE_DIR)
os.mkdir(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/")
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
src_paths = glob.glob(src_dir + "*.mhd")
'''
# for debug, dive into one image
for src_path in src_paths:
print(src_path)
pdb.set_trace() # use s to step in here
process_image(src_path)
'''
if only_process_patient is None and False:
pool = multiprocessing.Pool(settings.WORKER_POOL_SIZE)
pool.map(process_image, src_paths)
else:
for src_path in src_paths:
print(src_path)
if only_process_patient is not None:
if only_process_patient not in src_path:
continue
pdb.set_trace()
process_image(src_path)
#'''
# 3rd
def process_pos_annotations_patient2():
candidate_index = 0
only_patient = "197063290812663596858124411210"
only_patient = None
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if only_patient is not None and only_patient not in src_path:
continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print(candidate_index, " patient: ", patient_id)
process_pos_annotations_patient(src_path, patient_id)
candidate_index += 1
# 4th
def process_excluded_annotations_patients(only_patient=None):
candidate_index = 0
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if only_patient is not None and only_patient not in src_path:
continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print(candidate_index, " patient: ", patient_id)
process_excluded_annotations_patient(src_path, patient_id)
candidate_index += 1
# 6th
def process_auto_candidates_patients():
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for patient_index, src_path in enumerate(glob.glob(src_dir + "*.mhd")):
# if not "100621383016233746780170740405" in src_path:
# continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_index, " ", patient_id)
# process_auto_candidates_patient(src_path, patient_id, sample_count=500, candidate_type="white")
process_auto_candidates_patient(src_path, patient_id, sample_count=200, candidate_type="edge")
# 5th
def process_luna_candidates_patients(only_patient_id=None):
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for patient_index, src_path in enumerate(glob.glob(src_dir + "*.mhd")):
# if not "100621383016233746780170740405" in src_path:
# continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
if only_patient_id is not None and patient_id != only_patient_id:
continue
print("Patient: ", patient_index, " ", patient_id)
process_luna_candidates_patient(src_path, patient_id)
# 2nd output: lidc_annotations.csv (input: lidc xml files)
def process_lidc_annotations(only_patient=None, agreement_threshold=0):
# lines.append(",".join())
file_no = 0
pos_count = 0
neg_count = 0
all_lines = []
for anno_dir in [d for d in glob.glob("resources/luna16_annotations/*") if os.path.isdir(d)]:
xml_paths = glob.glob(anno_dir + "/*.xml")
for xml_path in xml_paths:
print(file_no, ": ", xml_path)
pos, neg, extended = load_lidc_xml(xml_path=xml_path, only_patient=only_patient, agreement_threshold=agreement_threshold)
if pos is not None:
pos_count += len(pos)
neg_count += len(neg)
print("Pos: ", pos_count, " Neg: ", neg_count)
file_no += 1
all_lines += extended
# if file_no > 10:
# break
# extended_line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy, sphericiy, margin, spiculation, texture, calcification, internal_structure, lobulation, subtlety ]
df_annos = pandas.DataFrame(all_lines, columns=["patient_id", "anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore", "sphericiy", "margin", "spiculation", "texture", "calcification", "internal_structure", "lobulation", "subtlety"])
df_annos.to_csv(settings.BASE_DIR + "lidc_annotations.csv", index=False)
if __name__ == "__main__":
if True:
# output: patient/image slices (input: luna_raw)
only_process_patient = None
process_images(delete_existing=False, only_process_patient=only_process_patient)
if False:
# -----------------------------------------------
# LIDC: enriched nodule information (LIDC coords)
# input: xml files
# output: csv files, lidc_annotations.csv
# patient_annos_pos_lidc.csv
# patient_annos_neg_lidc.csv
# ----------------------------------------------
process_lidc_annotations(only_patient=None, agreement_threshold=0)
if False:
# -----------------------------------------------
# Luna:simplified nodule information (Luna coords)
# input: luna_raw / annotations.csv
# output(luna2lidc): patient_annos_pos.csv
# -----------------------------------------------
process_pos_annotations_patient2()
# -----------------------------------------------
# Luna:simplified nodule information (Luna coords)
# input: luna_raw / annotations_excluded.csv
# output(luna2lidc): patient_annos_excluded.csv
# -----------------------------------------------
process_excluded_annotations_patients(only_patient=None)
if True:
# -----------------------------------------------
# Luna:simplified nodule information (Luna coords)
# input: luna_raw/ candidates_V2.csv
# output(luna2lidc): patient_candidates_luna.csv
# -----------------------------------------------
process_luna_candidates_patients(only_patient_id=None)
if False:
# -----------------------------------------------
# 200 non-module rois
# input: patient_annos_pos_lidc.csv
# patient_manual.csv
#
# output: patient_candidates_edge.csv
# -----------------------------------------------
process_auto_candidates_patients()