测试_qxy
In [1]:
import glob
import numpy as np
import os
import shutil
import matplotlib
import matplotlib.pyplot as plt
In [14]:
fp_list = [os.path.join(r,f[i]) for r,d,f in os.walk(r"D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData") if d==[]
for i in range(len(f))]
fp_list = sorted(fp_list)
Out[14]:
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'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\Brats17_TCIA_328_1_flair.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\Brats17_TCIA_328_1_seg.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\Brats17_TCIA_328_1_t1.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\Brats17_TCIA_328_1_t1ce.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\Brats17_TCIA_328_1_t2.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_328_1\\ROI_Brats17_TCIA_328_1_t1.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\Brats17_TCIA_331_1_flair.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\Brats17_TCIA_331_1_seg.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\Brats17_TCIA_331_1_t1.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\Brats17_TCIA_331_1_t1ce.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\Brats17_TCIA_331_1_t2.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_331_1\\ROI_Brats17_TCIA_331_1_t1.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_332_1\\Brats17_TCIA_332_1_flair.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_332_1\\Brats17_TCIA_332_1_seg.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_332_1\\Brats17_TCIA_332_1_t1.nii.gz', 'D:\\programe\\脑影像分割\\data\\BRATS2017\\Brats17TrainingData\\HGG\\Brats17_TCIA_332_1\\Brats17_TCIA_332_1_t1ce.nii.gz', ...]
In [41]:
#创建文件夹
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t1')
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t2')
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\flair')
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t1ce')
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\seg')
os.mkdir(r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\ROI')
In [50]:
for i in range(len(fp_list)):
# if fp_list[i].split("_")[-1].split(".")[0]== "flair":
# shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\flair')
if fp_list[i].split("_")[-1].split(".")[0]== "t1":
shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t1')
if fp_list[i].split("_")[-1].split(".")[0]== "t1ce":
shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t1ce')
if fp_list[i].split("_")[-1].split(".")[0]== "t2":
shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\t2')
if fp_list[i].split("_")[-1].split(".")[0]== "seg":
shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\seg')
# else:
# shutil.move(fp_list[i], r'D:\programe\脑影像分割\data\BRATS2017\Brats17TrainingData\ROI')
In [104]:
import matplotlib
matplotlib.use('TkAgg')
import SimpleITK as sitk
from matplotlib import pylab as plt
import nibabel as nib
from nibabel import nifti1
from nibabel.viewers import OrthoSlicer3D
In [28]:
import SimpleITK as sitk
import matplotlib.pyplot as plt
# Load image
sitkImage = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1\Brats17_2013_0_1_t1.nii.gz')
# Convert image to numpy array
# array = sitk.GetArrayFromImage(image)
# z = int(sitkImage.GetSize()[2]/2)
slice = sitk.GetArrayFromImage(sitkImage)[100,:,:]
plt.figure(figsize=(5,5))
plt.imshow(slice, 'gray')
plt.show()
print("Nifit文件读取成功，Size为：{}".format(sitkImage.GetSize()))
Nifit文件读取成功，Size为：(240, 240, 155)
In [105]:
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
# 读取三个模态的数据
img_t1 = nib.load(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1\Brats17_2013_0_1_t1.nii.gz')
data_t1 = img_t1.get_fdata()
img_t2 = nib.load(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t2\Brats17_2013_0_1_t2.nii.gz')
data_t2 = img_t2.get_fdata()
img_flair = nib.load(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\flair\Brats17_2013_0_1_flair.nii.gz')
data_flair = img_flair.get_fdata()
img_t1ce = nib.load(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1ce\Brats17_2013_0_1_t1ce.nii.gz')
data_t1ce = img_t1ce.get_fdata()
img_seg = nib.load(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\seg\Brats17_2013_0_1_seg.nii.gz')
data_seg = img_seg.get_fdata()
# data_t1 = sitk.GetArrayFromImage(aligned_t1)
# data_t2 = sitk.GetArrayFromImage(aligned_t2)
# data_flair = sitk.GetArrayFromImage(aligned_flair)
# data_t1ce = sitk.GetArrayFromImage(aligned_t1ce)
# data_seg = sitk.GetArrayFromImage(aligned_seg)
# 创建初始图形
fig, axs = plt.subplots(1, 5, figsize=(10, 4))
slices = [data_t1.shape[2]//2, data_t2.shape[2]//2, data_flair.shape[2]//2, data_t1ce.shape[2]//2, data_seg.shape[2]//2]
# 在子图中显示每个模态数据的切片
im_t1 = axs[0].imshow(data_t1[:, :, slices[0]], cmap='gray')
axs[0].set_title('T1')
im_t2 = axs[1].imshow(data_t2[:, :, slices[1]], cmap='gray')
axs[1].set_title('T2')
im_flair = axs[2].imshow(data_flair[:, :, slices[2]], cmap='gray')
axs[2].set_title('FLAIR')
im_t1ce = axs[3].imshow(data_t1ce[:, :, slices[3]], cmap='gray')
axs[3].set_title('T1CE')
im_seg = axs[4].imshow(data_seg[:, :, slices[4]], cmap='gray')
axs[4].set_title('SEG')
# 定义轴向切片回调函数
def update_slice(axis, val):
slices[axis] = int(val)
for i in range(5):
if i == axis:
if i == 0:
im_t1.set_data(data_t1[:, :, slices[i]])
elif i == 1:
im_t2.set_data(data_t2[:, :, slices[i]])
elif i == 2:
im_flair.set_data(data_flair[:, :, slices[i]])
elif i == 3:
im_t1ce.set_data(data_t1ce[:, :, slices[i]])
elif i == 4:
im_seg.set_data(data_seg[:, :, slices[i]])
axs[i].set_title(['T1', 'T2', 'FLAIR','T1CE','SEG'][i])
fig.canvas.draw_idle()
# 创建滑动条
ax_t1 = plt.axes([0.1, 0.05, 0.8, 0.02])
slider_t1 = Slider(ax_t1, 'T1 slice', 0, data_t1.shape[2]-1, valinit=slices[0], valstep=1)
slider_t1.on_changed(lambda val: update_slice(0, val))
ax_t2 = plt.axes([0.1, 0.025, 0.8, 0.02])
slider_t2 = Slider(ax_t2, 'T2 slice', 0, data_t2.shape[2]-1, valinit=slices[1], valstep=1)
slider_t2.on_changed(lambda val: update_slice(1, val))
ax_flair = plt.axes([0.1, 0.0, 0.8, 0.02])
slider_flair = Slider(ax_flair, 'FLAIR slice', 0, data_flair.shape[2]-1, valinit=slices[2], valstep=1)
slider_flair.on_changed(lambda val: update_slice(2, val))
ax_t1ce = plt.axes([0.1, 0.075, 0.8, 0.02])
slider_t1ce = Slider(ax_t1ce, 'T1CE slice', 0, data_t1ce.shape[2]-1, valinit=slices[3], valstep=1)
slider_t1ce.on_changed(lambda val: update_slice(3, val))
ax_seg = plt.axes([0.1, 0.1, 0.8, 0.02])
slider_seg = Slider(ax_seg, 'SEG slice', 0, data_seg.shape[2]-1, valinit=slices[4], valstep=1)
slider_seg.on_changed(lambda val: update_slice(4, val))
plt.show()
In [106]:
# 加载MRI图像和标签数据
def load_data():
t1 = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1\Brats17_2013_0_1_t1.nii.gz')
t1ce = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1ce\Brats17_2013_0_1_t1ce.nii.gz')
t2 = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t2\Brats17_2013_0_1_t2.nii.gz')
flair = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\flair\Brats17_2013_0_1_flair.nii.gz')
seg = sitk.ReadImage(r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\seg\Brats17_2013_0_1_seg.nii.gz')
return t1, t1ce, t2, flair, seg
t1, t1ce, t2, flair, seg = load_data()
fixed_image = t1 # 以T1为固定图像
transform = sitk.CenteredTransformInitializer(fixed_image, seg, sitk.Euler3DTransform(), sitk.CenteredTransformInitializerFilter.GEOMETRY)
# 对四个模态和分割标签数据进行对齐
t1 = sitk.Resample(t1, fixed_image, transform, sitk.sitkLinear, 0.0, t1.GetPixelID())
t2 = sitk.Resample(t2, fixed_image, transform, sitk.sitkLinear, 0.0, t2.GetPixelID())
flair = sitk.Resample(flair, fixed_image, transform, sitk.sitkLinear, 0.0, flair.GetPixelID())
t1ce = sitk.Resample(t1ce, fixed_image, transform, sitk.sitkLinear, 0.0, t1ce.GetPixelID())
seg = sitk.Resample(seg, fixed_image, transform, sitk.sitkNearestNeighbor, 0.0, seg.GetPixelID())
import matplotlib.pyplot as plt
# 可视化T1模态
plt.figure()
plt.imshow(sitk.GetArrayFromImage(t1)[80,:,:], cmap='gray')
plt.axis('off')
plt.title('T1')
# 可视化T2模态
plt.figure()
plt.imshow(sitk.GetArrayFromImage(t2)[80,:,:], cmap='gray')
plt.axis('off')
plt.title('T2')
# 可视化FLAIR模态
plt.figure()
plt.imshow(sitk.GetArrayFromImage(flair)[80,:,:], cmap='gray')
plt.axis('off')
plt.title('FLAIR')
# 可视化T1CE模态
plt.figure()
plt.imshow(sitk.GetArrayFromImage(t1ce)[80,:,:], cmap='gray')
plt.axis('off')
plt.title('T1CE')
# 可视化分割标签
plt.figure()
plt.imshow(sitk.GetArrayFromImage(seg)[80,:,:], cmap='gray')
plt.axis('off')
plt.title('Segmentation')
plt.show()
In [78]:
### 分割标签叠加
# 将图像和分割标签转换为numpy数组
t1_arr = sitk.GetArrayFromImage(t1)
t2_arr = sitk.GetArrayFromImage(t2)
flair_arr = sitk.GetArrayFromImage(flair)
t1ce_arr = sitk.GetArrayFromImage(t1ce)
seg_arr = sitk.GetArrayFromImage(seg)
# 将分割标签叠加到每个模态上
t1_overlay = np.ma.masked_where(seg_arr == 0, t1_arr)
t2_overlay = np.ma.masked_where(seg_arr == 0, t2_arr)
flair_overlay = np.ma.masked_where(seg_arr == 0, flair_arr)
t1ce_overlay = np.ma.masked_where(seg_arr == 0, t1ce_arr)
# 可视化
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))
axes[0, 0].imshow(t1_arr[80,:,:], cmap='gray')
axes[0, 0].set_title('T1')
axes[0, 1].imshow(t2_arr[80,:,:], cmap='gray')
axes[0, 1].set_title('T2')
axes[0, 2].imshow(flair_arr[80,:,:], cmap='gray')
axes[0, 2].set_title('FLAIR')
axes[0, 3].imshow(t1ce_arr[80,:,:], cmap='gray')
axes[0, 3].set_title('T1CE')
axes[1, 0].imshow(t1_overlay[80,:,:], cmap='jet')
axes[1, 0].set_title('T1 with segmentation')
axes[1, 1].imshow(t2_overlay[80,:,:], cmap='jet')
axes[1, 1].set_title('T2 with segmentation')
axes[1, 2].imshow(flair_overlay[80,:,:], cmap='jet')
axes[1, 2].set_title('FLAIR with segmentation')
axes[1, 3].imshow(t1ce_overlay[80,:,:], cmap='jet')
axes[1, 3].set_title('T1CE with segmentation')
for ax in axes.flatten():
ax.axis('off')
plt.show()
In [107]:
# 统计T1CE图像像素值的分布
intensity_stats = sitk.StatisticsImageFilter()
intensity_stats.Execute(t2)
# 输出T1CE图像的像素值分布信息
print("T1CE intensity distribution:")
print("Minimum: ", intensity_stats.GetMinimum())
print("Maximum: ", intensity_stats.GetMaximum())
print("Mean: ", intensity_stats.GetMean())
print("Standard deviation: ", intensity_stats.GetSigma())
# 绘制T1CE图像像素值的直方图
plt.hist(t2_arr.flatten(), bins=50, color='b')
plt.title('T1CE intensity histogram')
plt.xlabel('Intensity')
plt.ylabel('Frequency')
plt.show()
T1CE intensity distribution: Minimum: 0.0 Maximum: 1673.0 Mean: 62.790599238351255 Standard deviation: 161.17251482950365
In [108]:
2D图像¶
In [109]:
outputImg_path = r"D:\programe\brain\data\trainImage"
outputMask_path = r"D:\programe\brain\data\trainMask"
if not os.path.exists(outputImg_path):
os.mkdir(outputImg_path)
if not os.path.exists(outputMask_path):
os.mkdir(outputMask_path)
In [110]:
def normalize(slice, bottom=99, down=1):
"""
normalize image with mean and std for regionnonzero,and clip the value into range
:param slice:
:param bottom:
:param down:
:return:
"""
#有点像“去掉最低分去掉最高分”的意思,使得数据集更加“公平”
b = np.percentile(slice, bottom)
t = np.percentile(slice, down)
slice = np.clip(slice, t, b)#限定范围numpy.clip(a, a_min, a_max, out=None)
#除了黑色背景外的区域要进行标准化
image_nonzero = slice[np.nonzero(slice)]
if np.std(slice) == 0 or np.std(image_nonzero) == 0:
return slice
else:
tmp = (slice - np.mean(image_nonzero)) / np.std(image_nonzero)
# since the range of intensities is between 0 and 5000 ,
# the min in the normalized slice corresponds to 0 intensity in unnormalized slice
# the min is replaced with -9 just to keep track of 0 intensities
# so that we can discard those intensities afterwards when sampling random patches
tmp[tmp == tmp.min()] = -9 #黑色背景区域
return tmp
def crop_ceter(img,croph,cropw):
#for n_slice in range(img.shape[0]):
height,width = img[0].shape
starth = height//2-(croph//2)
startw = width//2-(cropw//2)
return img[:,starth:starth+croph,startw:startw+cropw]
In [1]:
# 设置文件夹路径
mri_flair= r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\flair'
mri_t1 = r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1'
mri_t2= r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t2'
mri_t1ce= r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\t1ce'
mri_seg = r'D:\programe\brain\data\BRATS2017\Brats17TrainingData\seg'
In [125]:
name = os.listdir(mri_flair)
name
Out[125]:
['Brats17_2013_0_1_flair.nii.gz', 'Brats17_2013_10_1_flair.nii.gz', 'Brats17_2013_11_1_flair.nii.gz', 'Brats17_2013_12_1_flair.nii.gz', 'Brats17_2013_13_1_flair.nii.gz', 'Brats17_2013_14_1_flair.nii.gz', 'Brats17_2013_15_1_flair.nii.gz', 'Brats17_2013_16_1_flair.nii.gz', 'Brats17_2013_17_1_flair.nii.gz', 'Brats17_2013_18_1_flair.nii.gz', 'Brats17_2013_19_1_flair.nii.gz', 'Brats17_2013_1_1_flair.nii.gz', 'Brats17_2013_20_1_flair.nii.gz', 'Brats17_2013_21_1_flair.nii.gz', 'Brats17_2013_22_1_flair.nii.gz', 'Brats17_2013_23_1_flair.nii.gz', 'Brats17_2013_24_1_flair.nii.gz', 'Brats17_2013_25_1_flair.nii.gz', 'Brats17_2013_26_1_flair.nii.gz', 'Brats17_2013_27_1_flair.nii.gz', 'Brats17_2013_28_1_flair.nii.gz', 'Brats17_2013_29_1_flair.nii.gz', 'Brats17_2013_2_1_flair.nii.gz', 'Brats17_2013_3_1_flair.nii.gz', 'Brats17_2013_4_1_flair.nii.gz', 'Brats17_2013_5_1_flair.nii.gz', 'Brats17_2013_6_1_flair.nii.gz', 'Brats17_2013_7_1_flair.nii.gz', 'Brats17_2013_8_1_flair.nii.gz', 'Brats17_2013_9_1_flair.nii.gz', 'Brats17_CBICA_AAB_1_flair.nii.gz', 'Brats17_CBICA_AAG_1_flair.nii.gz', 'Brats17_CBICA_AAL_1_flair.nii.gz', 'Brats17_CBICA_AAP_1_flair.nii.gz', 'Brats17_CBICA_ABB_1_flair.nii.gz', 'Brats17_CBICA_ABE_1_flair.nii.gz', 'Brats17_CBICA_ABM_1_flair.nii.gz', 'Brats17_CBICA_ABN_1_flair.nii.gz', 'Brats17_CBICA_ABO_1_flair.nii.gz', 'Brats17_CBICA_ABY_1_flair.nii.gz', 'Brats17_CBICA_ALN_1_flair.nii.gz', 'Brats17_CBICA_ALU_1_flair.nii.gz', 'Brats17_CBICA_ALX_1_flair.nii.gz', 'Brats17_CBICA_AME_1_flair.nii.gz', 'Brats17_CBICA_AMH_1_flair.nii.gz', 'Brats17_CBICA_ANG_1_flair.nii.gz', 'Brats17_CBICA_ANI_1_flair.nii.gz', 'Brats17_CBICA_ANP_1_flair.nii.gz', 'Brats17_CBICA_ANZ_1_flair.nii.gz', 'Brats17_CBICA_AOD_1_flair.nii.gz', 'Brats17_CBICA_AOH_1_flair.nii.gz', 'Brats17_CBICA_AOO_1_flair.nii.gz', 'Brats17_CBICA_AOP_1_flair.nii.gz', 'Brats17_CBICA_AOZ_1_flair.nii.gz', 'Brats17_CBICA_APR_1_flair.nii.gz', 'Brats17_CBICA_APY_1_flair.nii.gz', 'Brats17_CBICA_APZ_1_flair.nii.gz', 'Brats17_CBICA_AQA_1_flair.nii.gz', 'Brats17_CBICA_AQD_1_flair.nii.gz', 'Brats17_CBICA_AQG_1_flair.nii.gz', 'Brats17_CBICA_AQJ_1_flair.nii.gz', 'Brats17_CBICA_AQN_1_flair.nii.gz', 'Brats17_CBICA_AQO_1_flair.nii.gz', 'Brats17_CBICA_AQP_1_flair.nii.gz', 'Brats17_CBICA_AQQ_1_flair.nii.gz', 'Brats17_CBICA_AQR_1_flair.nii.gz', 'Brats17_CBICA_AQT_1_flair.nii.gz', 'Brats17_CBICA_AQU_1_flair.nii.gz', 'Brats17_CBICA_AQV_1_flair.nii.gz', 'Brats17_CBICA_AQY_1_flair.nii.gz', 'Brats17_CBICA_AQZ_1_flair.nii.gz', 'Brats17_CBICA_ARF_1_flair.nii.gz', 'Brats17_CBICA_ARW_1_flair.nii.gz', 'Brats17_CBICA_ARZ_1_flair.nii.gz', 'Brats17_CBICA_ASA_1_flair.nii.gz', 'Brats17_CBICA_ASE_1_flair.nii.gz', 'Brats17_CBICA_ASG_1_flair.nii.gz', 'Brats17_CBICA_ASH_1_flair.nii.gz', 'Brats17_CBICA_ASK_1_flair.nii.gz', 'Brats17_CBICA_ASN_1_flair.nii.gz', 'Brats17_CBICA_ASO_1_flair.nii.gz', 'Brats17_CBICA_ASU_1_flair.nii.gz', 'Brats17_CBICA_ASV_1_flair.nii.gz', 'Brats17_CBICA_ASW_1_flair.nii.gz', 'Brats17_CBICA_ASY_1_flair.nii.gz', 'Brats17_CBICA_ATB_1_flair.nii.gz', 'Brats17_CBICA_ATD_1_flair.nii.gz', 'Brats17_CBICA_ATF_1_flair.nii.gz', 'Brats17_CBICA_ATP_1_flair.nii.gz', 'Brats17_CBICA_ATV_1_flair.nii.gz', 'Brats17_CBICA_ATX_1_flair.nii.gz', 'Brats17_CBICA_AUN_1_flair.nii.gz', 'Brats17_CBICA_AUQ_1_flair.nii.gz', 'Brats17_CBICA_AUR_1_flair.nii.gz', 'Brats17_CBICA_AVG_1_flair.nii.gz', 'Brats17_CBICA_AVJ_1_flair.nii.gz', 'Brats17_CBICA_AVV_1_flair.nii.gz', 'Brats17_CBICA_AWG_1_flair.nii.gz', 'Brats17_CBICA_AWH_1_flair.nii.gz', 'Brats17_CBICA_AWI_1_flair.nii.gz', 'Brats17_CBICA_AXJ_1_flair.nii.gz', 'Brats17_CBICA_AXL_1_flair.nii.gz', 'Brats17_CBICA_AXM_1_flair.nii.gz', 'Brats17_CBICA_AXN_1_flair.nii.gz', 'Brats17_CBICA_AXO_1_flair.nii.gz', 'Brats17_CBICA_AXQ_1_flair.nii.gz', 'Brats17_CBICA_AXW_1_flair.nii.gz', 'Brats17_CBICA_AYA_1_flair.nii.gz', 'Brats17_CBICA_AYI_1_flair.nii.gz', 'Brats17_CBICA_AYU_1_flair.nii.gz', 'Brats17_CBICA_AYW_1_flair.nii.gz', 'Brats17_CBICA_AZD_1_flair.nii.gz', 'Brats17_CBICA_AZH_1_flair.nii.gz', 'Brats17_CBICA_BFB_1_flair.nii.gz', 'Brats17_CBICA_BFP_1_flair.nii.gz', 'Brats17_CBICA_BHB_1_flair.nii.gz', 'Brats17_CBICA_BHK_1_flair.nii.gz', 'Brats17_CBICA_BHM_1_flair.nii.gz', 'Brats17_TCIA_101_1_flair.nii.gz', 'Brats17_TCIA_103_1_flair.nii.gz', 'Brats17_TCIA_105_1_flair.nii.gz', 'Brats17_TCIA_109_1_flair.nii.gz', 'Brats17_TCIA_111_1_flair.nii.gz', 'Brats17_TCIA_113_1_flair.nii.gz', 'Brats17_TCIA_117_1_flair.nii.gz', 'Brats17_TCIA_118_1_flair.nii.gz', 'Brats17_TCIA_121_1_flair.nii.gz', 'Brats17_TCIA_130_1_flair.nii.gz', 'Brats17_TCIA_131_1_flair.nii.gz', 'Brats17_TCIA_133_1_flair.nii.gz', 'Brats17_TCIA_135_1_flair.nii.gz', 'Brats17_TCIA_138_1_flair.nii.gz', 'Brats17_TCIA_141_1_flair.nii.gz', 'Brats17_TCIA_147_1_flair.nii.gz', 'Brats17_TCIA_149_1_flair.nii.gz', 'Brats17_TCIA_150_1_flair.nii.gz', 'Brats17_TCIA_151_1_flair.nii.gz', 'Brats17_TCIA_152_1_flair.nii.gz', 'Brats17_TCIA_162_1_flair.nii.gz', 'Brats17_TCIA_165_1_flair.nii.gz', 'Brats17_TCIA_167_1_flair.nii.gz', 'Brats17_TCIA_168_1_flair.nii.gz', 'Brats17_TCIA_171_1_flair.nii.gz', 'Brats17_TCIA_175_1_flair.nii.gz', 'Brats17_TCIA_177_1_flair.nii.gz', 'Brats17_TCIA_179_1_flair.nii.gz', 'Brats17_TCIA_180_1_flair.nii.gz', 'Brats17_TCIA_184_1_flair.nii.gz', 'Brats17_TCIA_186_1_flair.nii.gz', 'Brats17_TCIA_190_1_flair.nii.gz', 'Brats17_TCIA_192_1_flair.nii.gz', 'Brats17_TCIA_198_1_flair.nii.gz', 'Brats17_TCIA_199_1_flair.nii.gz', 'Brats17_TCIA_201_1_flair.nii.gz', 'Brats17_TCIA_202_1_flair.nii.gz', 'Brats17_TCIA_203_1_flair.nii.gz', 'Brats17_TCIA_205_1_flair.nii.gz', 'Brats17_TCIA_208_1_flair.nii.gz', 'Brats17_TCIA_211_1_flair.nii.gz', 'Brats17_TCIA_218_1_flair.nii.gz', 'Brats17_TCIA_221_1_flair.nii.gz', 'Brats17_TCIA_222_1_flair.nii.gz', 'Brats17_TCIA_226_1_flair.nii.gz', 'Brats17_TCIA_231_1_flair.nii.gz', 'Brats17_TCIA_234_1_flair.nii.gz', 'Brats17_TCIA_235_1_flair.nii.gz', 'Brats17_TCIA_241_1_flair.nii.gz', 'Brats17_TCIA_242_1_flair.nii.gz', 'Brats17_TCIA_247_1_flair.nii.gz', 'Brats17_TCIA_249_1_flair.nii.gz', 'Brats17_TCIA_254_1_flair.nii.gz', 'Brats17_TCIA_255_1_flair.nii.gz', 'Brats17_TCIA_257_1_flair.nii.gz', 'Brats17_TCIA_261_1_flair.nii.gz', 'Brats17_TCIA_265_1_flair.nii.gz', 'Brats17_TCIA_266_1_flair.nii.gz', 'Brats17_TCIA_274_1_flair.nii.gz', 'Brats17_TCIA_276_1_flair.nii.gz', 'Brats17_TCIA_277_1_flair.nii.gz', 'Brats17_TCIA_278_1_flair.nii.gz', 'Brats17_TCIA_280_1_flair.nii.gz', 'Brats17_TCIA_282_1_flair.nii.gz', 'Brats17_TCIA_283_1_flair.nii.gz', 'Brats17_TCIA_290_1_flair.nii.gz', 'Brats17_TCIA_296_1_flair.nii.gz', 'Brats17_TCIA_298_1_flair.nii.gz', 'Brats17_TCIA_299_1_flair.nii.gz', 'Brats17_TCIA_300_1_flair.nii.gz', 'Brats17_TCIA_307_1_flair.nii.gz', 'Brats17_TCIA_309_1_flair.nii.gz', 'Brats17_TCIA_310_1_flair.nii.gz', 'Brats17_TCIA_312_1_flair.nii.gz', 'Brats17_TCIA_314_1_flair.nii.gz', 'Brats17_TCIA_319_1_flair.nii.gz', 'Brats17_TCIA_321_1_flair.nii.gz', 'Brats17_TCIA_322_1_flair.nii.gz', 'Brats17_TCIA_325_1_flair.nii.gz', 'Brats17_TCIA_328_1_flair.nii.gz', 'Brats17_TCIA_330_1_flair.nii.gz', 'Brats17_TCIA_331_1_flair.nii.gz', 'Brats17_TCIA_332_1_flair.nii.gz', 'Brats17_TCIA_335_1_flair.nii.gz', 'Brats17_TCIA_338_1_flair.nii.gz', 'Brats17_TCIA_343_1_flair.nii.gz', 'Brats17_TCIA_346_1_flair.nii.gz', 'Brats17_TCIA_351_1_flair.nii.gz', 'Brats17_TCIA_361_1_flair.nii.gz', 'Brats17_TCIA_368_1_flair.nii.gz', 'Brats17_TCIA_370_1_flair.nii.gz', 'Brats17_TCIA_372_1_flair.nii.gz', 'Brats17_TCIA_374_1_flair.nii.gz', 'Brats17_TCIA_375_1_flair.nii.gz', 'Brats17_TCIA_377_1_flair.nii.gz', 'Brats17_TCIA_378_1_flair.nii.gz', 'Brats17_TCIA_387_1_flair.nii.gz', 'Brats17_TCIA_390_1_flair.nii.gz', 'Brats17_TCIA_393_1_flair.nii.gz', 'Brats17_TCIA_394_1_flair.nii.gz', 'Brats17_TCIA_396_1_flair.nii.gz', 'Brats17_TCIA_401_1_flair.nii.gz', 'Brats17_TCIA_402_1_flair.nii.gz', 'Brats17_TCIA_406_1_flair.nii.gz', 'Brats17_TCIA_408_1_flair.nii.gz', 'Brats17_TCIA_409_1_flair.nii.gz', 'Brats17_TCIA_410_1_flair.nii.gz', 'Brats17_TCIA_411_1_flair.nii.gz', 'Brats17_TCIA_412_1_flair.nii.gz', 'Brats17_TCIA_413_1_flair.nii.gz', 'Brats17_TCIA_419_1_flair.nii.gz', 'Brats17_TCIA_420_1_flair.nii.gz', 'Brats17_TCIA_425_1_flair.nii.gz', 'Brats17_TCIA_428_1_flair.nii.gz', 'Brats17_TCIA_429_1_flair.nii.gz', 'Brats17_TCIA_430_1_flair.nii.gz', 'Brats17_TCIA_436_1_flair.nii.gz', 'Brats17_TCIA_437_1_flair.nii.gz', 'Brats17_TCIA_442_1_flair.nii.gz', 'Brats17_TCIA_444_1_flair.nii.gz', 'Brats17_TCIA_448_1_flair.nii.gz', 'Brats17_TCIA_449_1_flair.nii.gz', 'Brats17_TCIA_451_1_flair.nii.gz', 'Brats17_TCIA_455_1_flair.nii.gz', 'Brats17_TCIA_460_1_flair.nii.gz', 'Brats17_TCIA_462_1_flair.nii.gz', 'Brats17_TCIA_466_1_flair.nii.gz', 'Brats17_TCIA_469_1_flair.nii.gz', 'Brats17_TCIA_470_1_flair.nii.gz', 'Brats17_TCIA_471_1_flair.nii.gz', 'Brats17_TCIA_473_1_flair.nii.gz', 'Brats17_TCIA_474_1_flair.nii.gz', 'Brats17_TCIA_478_1_flair.nii.gz', 'Brats17_TCIA_479_1_flair.nii.gz', 'Brats17_TCIA_480_1_flair.nii.gz', 'Brats17_TCIA_490_1_flair.nii.gz', 'Brats17_TCIA_491_1_flair.nii.gz', 'Brats17_TCIA_493_1_flair.nii.gz', 'Brats17_TCIA_498_1_flair.nii.gz', 'Brats17_TCIA_499_1_flair.nii.gz', 'Brats17_TCIA_603_1_flair.nii.gz', 'Brats17_TCIA_605_1_flair.nii.gz', 'Brats17_TCIA_606_1_flair.nii.gz', 'Brats17_TCIA_607_1_flair.nii.gz', 'Brats17_TCIA_608_1_flair.nii.gz', 'Brats17_TCIA_615_1_flair.nii.gz', 'Brats17_TCIA_618_1_flair.nii.gz', 'Brats17_TCIA_620_1_flair.nii.gz', 'Brats17_TCIA_621_1_flair.nii.gz', 'Brats17_TCIA_623_1_flair.nii.gz', 'Brats17_TCIA_624_1_flair.nii.gz', 'Brats17_TCIA_625_1_flair.nii.gz', 'Brats17_TCIA_628_1_flair.nii.gz', 'Brats17_TCIA_629_1_flair.nii.gz', 'Brats17_TCIA_630_1_flair.nii.gz', 'Brats17_TCIA_632_1_flair.nii.gz', 'Brats17_TCIA_633_1_flair.nii.gz', 'Brats17_TCIA_634_1_flair.nii.gz', 'Brats17_TCIA_637_1_flair.nii.gz', 'Brats17_TCIA_639_1_flair.nii.gz', 'Brats17_TCIA_640_1_flair.nii.gz', 'Brats17_TCIA_642_1_flair.nii.gz', 'Brats17_TCIA_644_1_flair.nii.gz', 'Brats17_TCIA_645_1_flair.nii.gz', 'Brats17_TCIA_650_1_flair.nii.gz', 'Brats17_TCIA_653_1_flair.nii.gz', 'Brats17_TCIA_654_1_flair.nii.gz']
In [123]:
for subsetindex in range(len(os.listdir(mri_flair))):
flair = os.listdir(mri_flair)
index = flair[subsetindex].split(".")[0].split("_flair")[0]
#获取每个病例的四个模态及Mask的路径
flair_image = os.path.join(mri_flair, flair[subsetindex])
t1_image = os.path.join(mri_t1, index + '_t1.nii.gz')
t1ce_image = os.path.join(mri_t1ce, index + '_t1ce.nii.gz')
t2_image = os.path.join(mri_t2, index + '_t2.nii.gz')
mask_image = os.path.join(mri_seg, index + '_seg.nii.gz')
#获取每个病例的四个模态及Mask数据
flair_src = sitk.ReadImage(flair_image, sitk.sitkInt16)
t1_src = sitk.ReadImage(t1_image, sitk.sitkInt16)
t1ce_src = sitk.ReadImage(t1ce_image, sitk.sitkInt16)
t2_src = sitk.ReadImage(t2_image, sitk.sitkInt16)
mask = sitk.ReadImage(mask_image, sitk.sitkUInt8)
#GetArrayFromImage()可用于将SimpleITK对象转换为ndarray
flair_array = sitk.GetArrayFromImage(flair_src)
t1_array = sitk.GetArrayFromImage(t1_src)
t1ce_array = sitk.GetArrayFromImage(t1ce_src)
t2_array = sitk.GetArrayFromImage(t2_src)
mask_array = sitk.GetArrayFromImage(mask)
#对四个模态分别进行标准化,由于它们对比度不同
flair_array_nor = normalize(flair_array)
t1_array_nor = normalize(t1_array)
t1ce_array_nor = normalize(t1ce_array)
t2_array_nor = normalize(t2_array)
#裁剪(偶数才行)
flair_crop = crop_ceter(flair_array_nor,160,160)
t1_crop = crop_ceter(t1_array_nor,160,160)
t1ce_crop = crop_ceter(t1ce_array_nor,160,160)
t2_crop = crop_ceter(t2_array_nor,160,160)
mask_crop = crop_ceter(mask_array,160,160)
# print(str(pathhgg_list[subsetindex]))
#切片处理,并去掉没有病灶的切片
for n_slice in range(flair_crop.shape[0]):
if np.max(mask_crop[n_slice,:,:]) != 0:
maskImg = mask_crop[n_slice,:,:]
FourModelImageArray = np.zeros((flair_crop.shape[1],flair_crop.shape[2],4),np.float)
flairImg = flair_crop[n_slice,:,:]
flairImg = flairImg.astype(np.float)
FourModelImageArray[:,:,0] = flairImg
t1Img = t1_crop[n_slice,:,:]
t1Img = t1Img.astype(np.float)
FourModelImageArray[:,:,1] = t1Img
t1ceImg = t1ce_crop[n_slice,:,:]
t1ceImg = t1ceImg.astype(np.float)
FourModelImageArray[:,:,2] = t1ceImg
t2Img = t2_crop[n_slice,:,:]
t2Img = t2Img.astype(np.float)
FourModelImageArray[:,:,3] = t2Img
# if (len(os.listdir(outputImg_path))>0) | (len(os.listdir(outputMask_path)) > 0):
# os.remove(outputImg_path)
# os.remove(outputMask_path)
imagepath = outputImg_path + "\\" + str(flair[subsetindex].split(".")[0].split("_flair")[0]) + "_" + str(n_slice) + ".npy"
maskpath = outputMask_path + "\\" + str(flair[subsetindex].split(".")[0].split("_flair")[0]) + "_" + str(n_slice) + ".npy"
np.save(imagepath,FourModelImageArray)#(160,160,4) np.float dtype('float64')
np.save(maskpath,maskImg)# (160, 160) dtype('uint8') 值为0 1 2 4
print("Done！")
C:\Users\nanshu\AppData\Local\Temp\ipykernel_7608\1068772365.py:39: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations FourModelImageArray = np.zeros((flair_crop.shape[1],flair_crop.shape[2],4),np.float) C:\Users\nanshu\AppData\Local\Temp\ipykernel_7608\1068772365.py:41: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations flairImg = flairImg.astype(np.float) C:\Users\nanshu\AppData\Local\Temp\ipykernel_7608\1068772365.py:44: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations t1Img = t1Img.astype(np.float) C:\Users\nanshu\AppData\Local\Temp\ipykernel_7608\1068772365.py:47: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations t1ceImg = t1ceImg.astype(np.float) C:\Users\nanshu\AppData\Local\Temp\ipykernel_7608\1068772365.py:50: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations t2Img = t2Img.astype(np.float)
Done！
In [4]:
### 数据集切分评价分割准确性
import os
import shutil
from sklearn.model_selection import train_test_split
def split_data():
name = os.listdir(mri_flair)
train, test = train_test_split(name, test_size=0.2, random_state=42)
for train_index in train:
for train_path in os.listdir(r'D:\programe\brain\data\trainImage'):
if train_index.split(".")[0].split("_flair")[0] in train_path:
shutil.copy(os.path.join(r'D:\programe\brain\data\trainImage',train_path),r'D:\programe\brain\data\BraTs\train_MR')
shutil.copy(os.path.join(r'D:\programe\brain\data\trainMask',train_path),r'D:\programe\brain\data\BraTs\train_mask')
for test_index in test:
for test_path in os.listdir(r'D:\programe\brain\data\trainImage'):
if test_index.split(".")[0].split("_flair")[0] in test_path:
shutil.copy(os.path.join(r'D:\programe\brain\data\trainImage',test_path),r'D:\programe\brain\data\BraTs\test_MR')
shutil.copy(os.path.join(r'D:\programe\brain\data\trainMask',test_path),r'D:\programe\brain\data\BraTs\test_mask')
return (train, test)
(train, test)= split_data()
In [2]:
from glob import glob
glob("/nanshu_data/yaozhdata/brain/BraTs/train_MR/*")
Out[2]:
['/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AVJ_1_124.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_328_1_56.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_12_1_107.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_20_1_61.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ALU_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ATP_1_78.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_409_1_116.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQP_1_132.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABO_1_53.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_406_1_71.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABN_1_66.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_374_1_99.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_BFP_1_129.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ATB_1_112.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABO_1_58.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AWI_1_113.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_179_1_38.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASK_1_55.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_151_1_115.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_377_1_55.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_480_1_120.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_133_1_120.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQU_1_56.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_28_1_31.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_16_1_77.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_480_1_57.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AXJ_1_97.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_644_1_85.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABB_1_129.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_451_1_78.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_192_1_105.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_226_1_66.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_265_1_61.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_321_1_77.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_105_1_67.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_283_1_74.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQG_1_69.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_111_1_33.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AXO_1_57.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_152_1_90.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_179_1_89.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_328_1_91.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_118_1_41.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_290_1_74.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AOZ_1_87.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_11_1_108.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_478_1_62.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_436_1_79.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_462_1_92.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ARF_1_100.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_16_1_69.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AYW_1_120.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_APY_1_105.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_20_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQZ_1_61.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_460_1_54.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_606_1_90.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_201_1_90.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_265_1_100.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_412_1_73.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AYW_1_106.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_105_1_77.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AAL_1_90.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_621_1_64.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_131_1_117.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_361_1_67.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_480_1_46.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_162_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_20_1_123.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_149_1_39.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABY_1_79.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABO_1_104.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_406_1_79.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_235_1_84.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABE_1_124.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ATB_1_110.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_274_1_113.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_151_1_85.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_448_1_87.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_101_1_91.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_109_1_40.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABO_1_83.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AXO_1_110.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AME_1_84.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_109_1_53.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_255_1_80.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_9_1_89.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_361_1_57.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_22_1_98.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ALN_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_117_1_62.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ABY_1_116.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_221_1_128.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_BFP_1_96.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASO_1_122.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_444_1_73.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_5_1_127.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_118_1_64.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASU_1_113.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_361_1_88.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AVV_1_54.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_314_1_29.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AVJ_1_61.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASA_1_90.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_448_1_42.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_628_1_70.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_314_1_49.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_307_1_38.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ARF_1_81.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_480_1_96.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_20_1_75.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_653_1_100.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_109_1_77.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_3_1_74.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_135_1_98.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_221_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_387_1_94.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_620_1_76.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_298_1_96.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_634_1_53.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_437_1_82.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_642_1_99.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_105_1_50.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_401_1_110.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_BHM_1_52.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_118_1_44.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_462_1_103.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ARF_1_118.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQP_1_139.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AWI_1_73.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_634_1_89.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_28_1_69.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_645_1_57.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_499_1_68.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_314_1_67.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_442_1_46.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AAG_1_58.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_199_1_125.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_442_1_81.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_274_1_95.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_131_1_79.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_133_1_75.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_277_1_98.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_314_1_33.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_118_1_63.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_634_1_34.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_633_1_110.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_19_1_110.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AWH_1_128.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_368_1_126.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQP_1_122.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_325_1_128.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_644_1_126.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_12_1_112.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_615_1_60.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AVV_1_120.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AUN_1_94.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_3_1_68.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_9_1_80.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_628_1_59.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_131_1_53.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_624_1_85.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_628_1_37.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_8_1_92.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_150_1_47.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ATB_1_125.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_2013_20_1_80.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASG_1_70.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_290_1_93.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_444_1_74.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_368_1_44.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_179_1_63.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_361_1_55.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AXQ_1_124.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_179_1_69.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_221_1_72.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_314_1_32.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ASO_1_111.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AXO_1_80.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_ANP_1_76.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_TCIA_444_1_43.npy', '/nanshu_data/yaozhdata/brain/BraTs/train_MR/Brats17_CBICA_AQN_1_103.npy', 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