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import monai
import os
import numpy as np
from monai.transforms import (
Compose,
LoadImaged,
Rotate90d,
ScaleIntensityd,
EnsureChannelFirstd,
ResizeWithPadOrCropd,
DivisiblePadd,
ThresholdIntensityd,
NormalizeIntensityd,
SqueezeDimd,
ShiftIntensityd,
Identityd,
CenterSpatialCropd,
ScaleIntensityRanged,
)
from torch.utils.data import DataLoader
def get_file_list(data_pelvis_path, train_number, val_number, source='mr', target='ct'):
#list all files in the folder
file_list=[i for i in os.listdir(data_pelvis_path) if 'overview' not in i]
file_list_path=[os.path.join(data_pelvis_path,i) for i in file_list]
#list all ct and mr files in folder
source_file_list=[os.path.join(j,f'{source}.nii.gz') for j in file_list_path]
target_file_list=[os.path.join(j,f'{target}.nii.gz') for j in file_list_path] #mr
# Dict Version
# source -> image
# target -> label
train_ds = [{'source': i, 'target': j, 'A_paths': i, 'B_paths': j} for i, j in zip(source_file_list[0:train_number], target_file_list[0:train_number])]
val_ds = [{'source': i, 'target': j, 'A_paths': i, 'B_paths': j} for i, j in zip(source_file_list[-val_number:], target_file_list[-val_number:])]
print('all files in dataset:',len(file_list))
return train_ds, val_ds
def load_volumes(train_transforms,val_transforms,
train_crop_ds, val_crop_ds,
train_ds, val_ds,
saved_name_train=None, saved_name_val=None,
ifsave=False,ifcheck=False):
train_volume_ds = monai.data.Dataset(data=train_crop_ds, transform=train_transforms)
val_volume_ds = monai.data.Dataset(data=val_crop_ds, transform=val_transforms)
if ifsave:
save_volumes(train_ds, val_ds, saved_name_train, saved_name_val)
if ifcheck:
check_volumes(train_ds, train_volume_ds, val_volume_ds, val_ds)
return train_volume_ds,val_volume_ds
def crop_volumes(train_ds, val_ds,center_crop,resized_size=(512,512,None),pad='minimum'):
if center_crop>0:
crop=Compose([LoadImaged(keys=["source", "target"]),
EnsureChannelFirstd(keys=["source", "target"]),
CenterSpatialCropd(keys=["source", "target"], roi_size=(-1,-1,center_crop)),
])
train_crop_ds = monai.data.Dataset(data=train_ds, transform=crop)
val_crop_ds = monai.data.Dataset(data=val_ds, transform=crop)
print('center crop:',center_crop)
else:
crop=Compose([LoadImaged(keys=["source", "target"]),
EnsureChannelFirstd(keys=["source", "target"]),
])
train_crop_ds = monai.data.Dataset(data=train_ds, transform=crop)
val_crop_ds = monai.data.Dataset(data=val_ds, transform=crop)
return train_crop_ds, val_crop_ds
def get_transforms(configs, mode='train'):
normalize=configs.dataset.normalize
pad=configs.dataset.pad
resized_size=configs.dataset.resized_size
WINDOW_WIDTH=configs.dataset.WINDOW_WIDTH
WINDOW_LEVEL=configs.dataset.WINDOW_LEVEL
prob=configs.dataset.augmentationProb
background=configs.dataset.background
transform_list=[]
min, max=WINDOW_LEVEL-(WINDOW_WIDTH/2), WINDOW_LEVEL+(WINDOW_WIDTH/2)
transform_list.append(ThresholdIntensityd(keys=["target"], threshold=min, above=True, cval=background))
#transform_list.append(ThresholdIntensityd(keys=["target"], threshold=max, above=False, cval=-1000))
# filter the source images
# transform_list.append(ThresholdIntensityd(keys=["source"], threshold=configs.dataset.MRImax, above=False, cval=0))
if normalize=='zscore':
transform_list.append(NormalizeIntensityd(keys=["source", "target"], nonzero=False, channel_wise=True))
print('zscore normalization')
elif normalize=='minmax':
transform_list.append(ScaleIntensityd(keys=["source", "target"], minv=-1, maxv=1.0))
print('minmax normalization')
elif normalize=='scale4000':
transform_list.append(ScaleIntensityd(keys=["source"], minv=-1, maxv=1))
transform_list.append(ScaleIntensityd(keys=["target"], minv=0))
transform_list.append(ScaleIntensityd(keys=["target"], factor=-0.99975)) # x=x(1+factor)
print('scale1000 normalization')
elif normalize=='scale1000':
transform_list.append(ScaleIntensityd(keys=["source"], minv=0, maxv=1))
transform_list.append(ScaleIntensityd(keys=["target"], minv=0))
transform_list.append(ScaleIntensityd(keys=["target"], factor=-0.99975))
print('scale1000 normalization')
elif normalize=='inputonlyzscore':
transform_list.append(NormalizeIntensityd(keys=["source"], nonzero=False, channel_wise=True))
print('only normalize input MRI images')
elif normalize=='inputonlyminmax':
transform_list.append(ScaleIntensityd(keys=["source"], minv=configs.dataset.normmin, maxv=configs.dataset.normmax))
print('only normalize input MRI images')
elif normalize=='none':
print('no normalization')
transform_list.append(ResizeWithPadOrCropd(keys=["source", "target", "mask"], spatial_size=resized_size,mode=pad))
# transform_list.append(ScaleIntensityRanged(keys=["target"],a_min=WINDOW_LEVEL-(WINDOW_WIDTH/2), a_max=WINDOW_LEVEL+(WINDOW_WIDTH/2),b_min=0, b_max=1, clip=True))
if mode == 'train':
from monai.transforms import (
# data augmentation
RandRotated,
RandZoomd,
RandBiasFieldd,
RandAffined,
RandGridDistortiond,
RandGridPatchd,
RandShiftIntensityd,
RandGibbsNoised,
RandAdjustContrastd,
RandGaussianSmoothd,
RandGaussianSharpend,
RandGaussianNoised,
)
Aug=True
if Aug:
transform_list.append(RandRotated(keys=["source", "target", "mask"], range_x = 0.1, range_y = 0.1, range_z = 0.1, prob=prob, padding_mode="border", keep_size=True))
transform_list.append(RandZoomd(keys=["source", "target", "mask"], prob=prob, min_zoom=0.9, max_zoom=1.3,padding_mode= "minimum" ,keep_size=True))
transform_list.append(RandAffined(keys=["source", "target", "mask"],padding_mode="border" , prob=prob))
#transform_list.append(Rand3DElasticd(keys=["source", "target"], prob=prob, sigma_range=(5, 8), magnitude_range=(100, 200), spatial_size=None, mode='bilinear'))
intensityAug=False
if intensityAug:
print('intensity data augmentation is used')
transform_list.append(RandBiasFieldd(keys=["source"], degree=3, coeff_range=(0.0, 0.1), prob=prob)) # only apply to MRI images
transform_list.append(RandGaussianNoised(keys=["source"], prob=prob, mean=0.0, std=0.01))
transform_list.append(RandAdjustContrastd(keys=["source"], prob=prob, gamma=(0.5, 1.5)))
transform_list.append(RandShiftIntensityd(keys=["source"], prob=prob, offsets=20))
transform_list.append(RandGaussianSharpend(keys=["source"], alpha=(0.2, 0.8), prob=prob))
#transform_list.append(Rotate90d(keys=["source", "target"], k=3))
#transform_list.append(DivisiblePadd(keys=["source", "target"], k=div_size, mode="minimum"))
#transform_list.append(Identityd(keys=["source", "target"])) # do nothing for the no norm case
train_transforms = Compose(transform_list)
return train_transforms
def get_length(dataset, patch_batch_size):
loader=DataLoader(dataset, batch_size=1)
iterator = iter(loader)
sum_nslices=0
for idx in range(len(loader)):
check_data = next(iterator)
nslices=check_data['source'].shape[-1]
sum_nslices+=nslices
if sum_nslices%patch_batch_size==0:
return sum_nslices//patch_batch_size
else:
return sum_nslices//patch_batch_size+1
def check_volumes(train_ds, train_volume_ds, val_volume_ds, val_ds):
# use batch_size=1 to check the volumes because the input volumes have different shapes
train_loader = DataLoader(train_volume_ds, batch_size=1)
val_loader = DataLoader(val_volume_ds, batch_size=1)
train_iterator = iter(train_loader)
val_iterator = iter(val_loader)
print('check training data:')
idx=0
for idx in range(len(train_loader)):
try:
train_check_data = next(train_iterator)
ds_idx = idx * 1
current_item = train_ds[ds_idx]
current_name = os.path.basename(os.path.dirname(current_item['source']))
print(idx, current_name, 'image:', train_check_data['source'].shape, 'label:', train_check_data['target'].shape)
except:
ds_idx = idx * 1
current_item = train_ds[ds_idx]
current_name = os.path.basename(os.path.dirname(current_item['source']))
print('check data error! Check the input data:',current_name)
print("checked all training data.")
print('check validation data:')
idx=0
for idx in range(len(val_loader)):
try:
val_check_data = next(val_iterator)
ds_idx = idx * 1
current_item = val_ds[ds_idx]
current_name = os.path.basename(os.path.dirname(current_item['source']))
print(idx, current_name, 'image:', val_check_data['source'].shape, 'label:', val_check_data['target'].shape)
except:
ds_idx = idx * 1
current_item = val_ds[ds_idx]
current_name = os.path.basename(os.path.dirname(current_item['source']))
print('check data error! Check the input data:',current_name)
print("checked all validation data.")
def save_volumes(train_ds, val_ds, saved_name_train, saved_name_val):
shape_list_train=[]
shape_list_val=[]
# use the function of saving information before
for sample in train_ds:
name = os.path.basename(os.path.dirname(sample['source']))
shape_list_train.append({'patient': name})
for sample in val_ds:
name = os.path.basename(os.path.dirname(sample['source']))
shape_list_val.append({'patient': name})
np.savetxt(saved_name_train,shape_list_train,delimiter=',',fmt='%s',newline='\n') # f means format, r means raw string
np.savetxt(saved_name_val,shape_list_val,delimiter=',',fmt='%s',newline='\n') # f means format, r means raw string
def check_batch_data(train_loader,val_loader,train_patch_ds,val_volume_ds,train_batch_size,val_batch_size):
for idx, train_check_data in enumerate(train_loader):
ds_idx = idx * train_batch_size
current_item = train_patch_ds[ds_idx]
print('check train data:')
print(current_item, 'image:', train_check_data['source'].shape, 'label:', train_check_data['target'].shape)
for idx, val_check_data in enumerate(val_loader):
ds_idx = idx * val_batch_size
current_item = val_volume_ds[ds_idx]
print('check val data:')
print(current_item, 'image:', val_check_data['source'].shape, 'label:', val_check_data['target'].shape)
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