-
Notifications
You must be signed in to change notification settings - Fork 19
/
scribbles_generator.py
275 lines (245 loc) · 9.83 KB
/
scribbles_generator.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
# -*- coding: utf-8 -*-
# Author: Shuojue Yang (main contribution) and Xiangde Luo (minor modification for WORD and other datasets).
# Date: 16 Dec. 2021
# Implementation for simulation of the sparse scribble annotation based on the dense annotation for the WORD dataset and other datasets.
# # Reference:
# @article{luo2022scribbleseg,
# title={Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision},
# author={Xiangde Luo, Minhao Hu, Wenjun Liao, Shuwei Zhai, Tao Song, Guotai Wang, Shaoting Zhang},
# journal={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022},
# year={2022},
# pages={528--538}}
# @article{luo2022word,
# title={{WORD}: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image},
# author={Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang},
# journal={Medical Image Analysis},
# volume={82},
# pages={102642},
# year={2022},
# publisher={Elsevier}}
# @misc{wsl4mis2020,
# title={{WSL4MIS}},
# author={Luo, Xiangde},
# howpublished={\url{https://github.com/Luoxd1996/WSL4MIS}},
# year={2021}}
# If you have any questions, please contact Xiangde Luo (https://luoxd1996.github.io).
import glob
import math
import random
import sys
import cv2
import numpy as np
import SimpleITK as sitk
from PIL import Image
from scipy import ndimage
from skimage.morphology import skeletonize
sys.setrecursionlimit(1000000)
seed = 2022
np.random.seed(seed)
random.seed(seed)
def random_rotation(image, max_angle=15):
angle = np.random.uniform(-max_angle, max_angle)
img = Image.fromarray(image)
img_rotate = img.rotate(angle)
return img_rotate
def translate_img(img, x_shift, y_shift):
(height, width) = img.shape[:2]
matrix = np.float32([[1, 0, x_shift], [0, 1, y_shift]])
trans_img = cv2.warpAffine(img, matrix, (width, height))
return trans_img
def get_largest_two_component_2D(img, print_info=False, threshold=None):
"""
Get the largest two components of a binary volume
inputs:
img: the input 2D volume
threshold: a size threshold
outputs:
out_img: the output volume
"""
s = ndimage.generate_binary_structure(2, 2) # iterate structure
labeled_array, numpatches = ndimage.label(img, s) # labeling
sizes = ndimage.sum(img, labeled_array, range(1, numpatches+1))
sizes_list = [sizes[i] for i in range(len(sizes))]
sizes_list.sort()
if(print_info):
print('component size', sizes_list)
if(len(sizes) == 1):
out_img = [img]
else:
if(threshold):
max_size1 = sizes_list[-1]
max_label1 = np.where(sizes == max_size1)[0] + 1
if max_label1.shape[0] > 1:
max_label1 = max_label1[0]
component1 = labeled_array == max_label1
out_img = [component1]
for temp_size in sizes_list:
if(temp_size > threshold):
temp_lab = np.where(sizes == temp_size)[0] + 1
temp_cmp = labeled_array == temp_lab[0]
out_img.append(temp_cmp)
return out_img
else:
max_size1 = sizes_list[-1]
max_size2 = sizes_list[-2]
max_label1 = np.where(sizes == max_size1)[0] + 1
max_label2 = np.where(sizes == max_size2)[0] + 1
if max_label1.shape[0] > 1:
max_label1 = max_label1[0]
if max_label2.shape[0] > 1:
max_label2 = max_label2[0]
component1 = labeled_array == max_label1
component2 = labeled_array == max_label2
if(max_size2*10 > max_size1):
out_img = [component1, component2]
else:
out_img = [component1]
return out_img
class Cutting_branch(object):
def __init__(self):
self.lst_bifur_pt = 0
self.branch_state = 0
self.lst_branch_state = 0
self.direction2delta = {0: [-1, -1], 1: [-1, 0], 2: [-1, 1], 3: [
0, -1], 4: [0, 0], 5: [0, 1], 6: [1, -1], 7: [1, 0], 8: [1, 1]}
def __find_start(self, lab):
y, x = lab.shape
idxes = np.asarray(np.nonzero(lab))
for i in range(idxes.shape[1]):
pt = tuple([idxes[0, i], idxes[1, i]])
assert lab[pt] == 1
directions = []
for d in range(9):
if d == 4:
continue
if self.__detect_pt_bifur_state(lab, pt, d):
directions.append(d)
if len(directions) == 1:
start = pt
self.start = start
self.output[start] = 1
return start
start = tuple([idxes[0, 0], idxes[1, 0]])
self.output[start] = 1
self.start = start
return start
def __detect_pt_bifur_state(self, lab, pt, direction):
d = direction
y = pt[0] + self.direction2delta[d][0]
x = pt[1] + self.direction2delta[d][1]
if lab[y, x] > 0:
return True
else:
return False
def __detect_neighbor_bifur_state(self, lab, pt):
directions = []
for i in range(9):
if i == 4:
continue
if self.output[tuple([pt[0] + self.direction2delta[i][0], pt[1] + self.direction2delta[i][1]])] > 0:
continue
if self.__detect_pt_bifur_state(lab, pt, i):
directions.append(i)
if len(directions) == 0:
self.end = pt
return False
else:
direction = random.sample(directions, 1)[0]
next_pt = tuple([pt[0] + self.direction2delta[direction]
[0], pt[1] + self.direction2delta[direction][1]])
if len(directions) > 1 and pt != self.start:
self.lst_output = self.output*1
self.previous_bifurPts.append(pt)
self.output[next_pt] = 1
pt = next_pt
self.__detect_neighbor_bifur_state(lab, pt)
def __detect_loop_branch(self, end):
for d in range(9):
if d == 4:
continue
y = end[0] + self.direction2delta[d][0]
x = end[1] + self.direction2delta[d][1]
if (y, x) in self.previous_bifurPts:
self.output = self.lst_output * 1
return True
def __call__(self, lab, seg_lab, iterations=1):
self.previous_bifurPts = []
self.output = np.zeros_like(lab)
self.lst_output = np.zeros_like(lab)
components = get_largest_two_component_2D(lab, threshold=15)
if len(components) > 1:
for c in components:
start = self.__find_start(c)
self.__detect_neighbor_bifur_state(c, start)
else:
c = components[0]
start = self.__find_start(c)
self.__detect_neighbor_bifur_state(c, start)
self.__detect_loop_branch(self.end)
struct = ndimage.generate_binary_structure(2, 2)
output = ndimage.morphology.binary_dilation(
self.output, structure=struct, iterations=iterations)
shift_y = random.randint(-6, 6)
shift_x = random.randint(-6, 6)
if np.sum(seg_lab) > 1000:
output = translate_img(output.astype(np.uint8), shift_x, shift_y)
output = random_rotation(output)
output = output * seg_lab
return output
def scrible_2d(label, iteration=[4, 10]):
lab = label
skeleton_map = np.zeros_like(lab, dtype=np.int32)
for i in range(lab.shape[0]):
if np.sum(lab[i]) == 0:
continue
struct = ndimage.generate_binary_structure(2, 2)
if np.sum(lab[i]) > 900 and iteration != 0 and iteration != [0] and iteration != None:
iter_num = math.ceil(
iteration[0]+random.random() * (iteration[1]-iteration[0]))
slic = ndimage.morphology.binary_erosion(
lab[i], structure=struct, iterations=iter_num)
else:
slic = lab[i]
sk_slice = skeletonize(slic, method='lee')
sk_slice = np.asarray((sk_slice == 255), dtype=np.int32)
skeleton_map[i] = sk_slice
return skeleton_map
def scribble4class(label, class_id, class_num, iteration=[4, 10], cut_branch=True):
label = (label == class_id)
sk_map = scrible_2d(label, iteration=iteration)
if cut_branch and class_id != 0:
cut = Cutting_branch()
for i in range(sk_map.shape[0]):
lab = sk_map[i]
if lab.sum() < 1:
continue
sk_map[i] = cut(lab, seg_lab=label[i])
if class_id == 0:
class_id = class_num
return sk_map * class_id
def generate_scribble(label, iterations, cut_branch=True):
class_num = np.max(label) + 1
output = np.zeros_like(label, dtype=np.uint8)
for i in range(class_num):
it = iterations[i] if isinstance(iterations, list) else iterations
scribble = scribble4class(
label, i, class_num, it, cut_branch=cut_branch)
output += scribble.astype(np.uint8)
return output
if __name__ == "__main__":
num = 0
for i in sorted(glob.glob("../imgs/*_lab.nii.gz")):
print("{} Begin".format(i.split("/")[-1]))
itk_data = sitk.ReadImage(i)
label = sitk.GetArrayFromImage(itk_data)
num_classes = 16 # total segmentation classes
output = generate_scribble(label, tuple([1, num_classes-1]))
# ignore index for partially cross-entropy loss
output[output == 0] = 255
output[output == num_classes] = 0
itk_scr = sitk.GetImageFromArray(output)
itk_scr.CopyInformation(itk_data)
sitk.WriteImage(itk_scr, i.replace('_lab.nii.gz', '_scribble.nii.gz'))
print("{} End".format(i.split("/")[-1]))
print(num)
num += 1