-
Notifications
You must be signed in to change notification settings - Fork 11
Expand file tree
/
Copy pathcompile_patientsamples.py
More file actions
172 lines (141 loc) · 8.72 KB
/
compile_patientsamples.py
File metadata and controls
172 lines (141 loc) · 8.72 KB
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
import os
import nibabel as nib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
#Load a spreadsheet with patient ID in the first column, IDH status in the second column, and age in the third column
xl = pd.ExcelFile('/home/spreadsheet.xlsx')
df = np.asarray(xl.parse("Sheet1"))
patient_IDH=df[:,[0,2]]
patient_age=df[:,[0,1]]
basedir = '/home/patients/'
os.chdir(basedir)
patients=next(os.walk('.'))[1]
desired_size=[142,142]
slices_FLAIR = np.empty([len(patients)*3, desired_size[0], desired_size[1], 3])
slices_T2 = np.empty([len(patients)*3, desired_size[0], desired_size[1], 3])
slices_T1 = np.empty([len(patients)*3, desired_size[0], desired_size[1], 3])
slices_T1post = np.empty([len(patients)*3, desired_size[0], desired_size[1], 3])
labels = np.empty(len(patients)*3)
age = np.empty(len(patients)*3)
def zoompad(array, desired_size):
array = cv2.resize(array,(desired_size[0],desired_size[1]))
return array
for p in range(len(patients)):
print(p, patients[p])
patient_dir = basedir + patients[p] + '/'
idx_idh=np.asarray(np.where((patient_IDH[:,0].astype(str))==str(patients[p])))
curr_idh = patient_IDH[idx_idh,1]
idx_age=np.asarray(np.where((patient_age[:,0].astype(str))==str(patients[p])))
curr_age = patient_age[idx_age,1]
os.chdir(patient_dir)
FLAIR = np.load('FLAIR_normssn4.npy')
T2 = np.load('T2_normssn4.npy')
T1 = np.load('T1_normssn4.npy')
T1post = np.load('T1post_normssn4.npy')
FLAIRmask = np.round(nib.load('FLAIRmask.nii').get_data()).astype(FLAIR.dtype)
FLAIR_m= FLAIR
T2_m= T2
T1_m= T1
T1post_m= T1post
#Find the largest, 75th, and 50th percentile slices in each dimension
x_sum=np.sum(FLAIRmask,axis=(1,2))
y_sum=np.sum(FLAIRmask,axis=(0,2))
z_sum=np.sum(FLAIRmask,axis=(0,1))
xp100=np.percentile(x_sum[np.nonzero(x_sum)],100,interpolation='nearest')
xp75=np.percentile(x_sum[np.nonzero(x_sum)],75,interpolation='nearest')
xp50=np.percentile(x_sum[np.nonzero(x_sum)],50,interpolation='nearest')
yp100=np.percentile(y_sum[np.nonzero(y_sum)],100,interpolation='nearest')
yp75=np.percentile(y_sum[np.nonzero(y_sum)],75,interpolation='nearest')
yp50=np.percentile(y_sum[np.nonzero(y_sum)],50,interpolation='nearest')
zp100=np.percentile(z_sum[np.nonzero(z_sum)],100,interpolation='nearest')
zp75=np.percentile(z_sum[np.nonzero(z_sum)],75,interpolation='nearest')
zp50=np.percentile(z_sum[np.nonzero(z_sum)],50,interpolation='nearest')
x_idx = np.argwhere(x_sum==xp100)[0][0]
y_idx = np.argwhere(y_sum==yp100)[0][0]
z_idx = np.argwhere(z_sum==zp100)[0][0]
B = np.argwhere(FLAIRmask[x_idx])
(xstart_x, ystart_x), (xstop_x, ystop_x) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,y_idx])
(xstart_y, ystart_y), (xstop_y, ystop_y) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,:,z_idx])
(xstart_z, ystart_z), (xstop_z, ystop_z) = B.min(0), B.max(0) + 1
FLAIR_x1 = zoompad(np.asarray(FLAIR_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
FLAIR_y1 = zoompad(np.asarray(FLAIR_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
FLAIR_z1 = zoompad(np.asarray(FLAIR_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T2_x1 = zoompad(np.asarray(T2_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T2_y1 = zoompad(np.asarray(T2_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T2_z1 = zoompad(np.asarray(T2_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1_x1 = zoompad(np.asarray(T1_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1_y1 = zoompad(np.asarray(T1_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1_z1 = zoompad(np.asarray(T1_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1post_x1 = zoompad(np.asarray(T1post_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1post_y1 = zoompad(np.asarray(T1post_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1post_z1 = zoompad(np.asarray(T1post_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
x_idx = np.argwhere(x_sum==xp75)[0][0]
y_idx = np.argwhere(y_sum==yp75)[0][0]
z_idx = np.argwhere(z_sum==zp75)[0][0]
B = np.argwhere(FLAIRmask[x_idx])
(xstart_x, ystart_x), (xstop_x, ystop_x) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,y_idx])
(xstart_y, ystart_y), (xstop_y, ystop_y) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,:,z_idx])
(xstart_z, ystart_z), (xstop_z, ystop_z) = B.min(0), B.max(0) + 1
FLAIR_x2 = zoompad(np.asarray(FLAIR_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
FLAIR_y2 = zoompad(np.asarray(FLAIR_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
FLAIR_z2 = zoompad(np.asarray(FLAIR_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T2_x2 = zoompad(np.asarray(T2_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T2_y2 = zoompad(np.asarray(T2_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T2_z2 = zoompad(np.asarray(T2_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1_x2 = zoompad(np.asarray(T1_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1_y2 = zoompad(np.asarray(T1_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1_z2 = zoompad(np.asarray(T1_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1post_x2 = zoompad(np.asarray(T1post_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1post_y2 = zoompad(np.asarray(T1post_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1post_z2 = zoompad(np.asarray(T1post_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
x_idx = np.argwhere(x_sum==xp50)[0][0]
y_idx = np.argwhere(y_sum==yp50)[0][0]
z_idx = np.argwhere(z_sum==zp50)[0][0]
B = np.argwhere(FLAIRmask[x_idx])
(xstart_x, ystart_x), (xstop_x, ystop_x) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,y_idx])
(xstart_y, ystart_y), (xstop_y, ystop_y) = B.min(0), B.max(0) + 1
B = np.argwhere(FLAIRmask[:,:,z_idx])
(xstart_z, ystart_z), (xstop_z, ystop_z) = B.min(0), B.max(0) + 1
FLAIR_x3 = zoompad(np.asarray(FLAIR_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
FLAIR_y3 = zoompad(np.asarray(FLAIR_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
FLAIR_z3 = zoompad(np.asarray(FLAIR_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T2_x3 = zoompad(np.asarray(T2_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T2_y3 = zoompad(np.asarray(T2_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T2_z3 = zoompad(np.asarray(T2_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1_x3 = zoompad(np.asarray(T1_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1_y3 = zoompad(np.asarray(T1_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1_z3 = zoompad(np.asarray(T1_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
T1post_x3 = zoompad(np.asarray(T1post_m[x_idx][xstart_x:xstop_x, ystart_x:ystop_x]), desired_size)
T1post_y3 = zoompad(np.asarray(T1post_m[:,y_idx][xstart_y:xstop_y, ystart_y:ystop_y]), desired_size)
T1post_z3 = zoompad(np.asarray(T1post_m[:,:,z_idx][xstart_z:xstop_z, ystart_z:ystop_z]), desired_size)
slices_FLAIR[3*p] = np.stack((FLAIR_x1, FLAIR_y2, FLAIR_z3), axis=2)
slices_FLAIR[3*p+1] = np.stack((FLAIR_x3, FLAIR_y1, FLAIR_z2), axis=2)
slices_FLAIR[3*p+2] = np.stack((FLAIR_x2, FLAIR_y3, FLAIR_z1), axis=2)
slices_T2[3*p] = np.stack((T2_x1, T2_y2, T2_z3), axis=2)
slices_T2[3*p+1] = np.stack((T2_x3, T2_y1, T2_z2), axis=2)
slices_T2[3*p+2] = np.stack((T2_x2, T2_y3, T2_z1), axis=2)
slices_T1[3*p] = np.stack((T1_x1, T1_y2, T1_z3), axis=2)
slices_T1[3*p+1] = np.stack((T1_x3, T1_y1, T1_z2), axis=2)
slices_T1[3*p+2] = np.stack((T1_x2, T1_y3, T1_z1), axis=2)
slices_T1post[3*p] = np.stack((T1post_x1, T1post_y2, T1post_z3), axis=2)
slices_T1post[3*p+1] = np.stack((T1post_x3, T1post_y1, T1post_z2), axis=2)
slices_T1post[3*p+2] = np.stack((T1post_x2, T1post_y3, T1post_z1), axis=2)
labels[3*p:3*p+3] = curr_idh
age[3*p:3*p+3] = curr_age
del FLAIR, T2, T1, T1post, FLAIRmask, curr_idh
#specify save directory
#save all patient data into numpy files
os.chdir('/home/savedirectory')
np.save('slices_FLAIR.npy', slices_FLAIR)
np.save('slices_T2.npy', slices_T2)
np.save('slices_T1.npy', slices_T1)
np.save('slices_T1post.npy', slices_T1post)
np.save('labels.npy',labels)
np.save('age.npy', age)