def initialize_transforms_simple(p=0.8):
transforms = [
RandomFlip(axes=(0, 1, 2), flip_probability=1, p=p),
#RandomAffine(scales=(0.9, 1.1), degrees=(10), isotropic=False,
# default_pad_value='otsu', image_interpolation=Interpolation.LINEAR,
# p = p, seed=None),
# *** SLOWS DOWN DATALOADER ***
#RandomElasticDeformation(num_control_points = 7, max_displacement = 7.5,
# locked_borders = 2, image_interpolation = Interpolation.LINEAR,
# p = 0.5, seed = None),
RandomMotion(degrees=10,
translation=10,
num_transforms=2,
image_interpolation='linear',
p=p),
RandomAnisotropy(axes=(0, 1, 2), downsampling=2),
RandomBiasField(coefficients=0.5, order=3, p=p),
RandomBlur(std=(0, 2), p=p),
RandomNoise(mean=0, std=(0, 5), p=p),
RescaleIntensity((0, 255))
]
transform = tio.Compose(transforms)
return transform
def test_transforms(self):
landmarks_dict = dict(
t1=np.linspace(0, 100, 13),
t2=np.linspace(0, 100, 13),
)
transforms = (
CenterCropOrPad((9, 21, 30)),
ToCanonical(),
Resample((1, 1.1, 1.25)),
RandomFlip(axes=(0, 1, 2), flip_probability=1),
RandomMotion(proportion_to_augment=1),
RandomGhosting(proportion_to_augment=1, axes=(0, 1, 2)),
RandomSpike(),
RandomNoise(),
RandomBlur(),
RandomSwap(patch_size=2, num_iterations=5),
Lambda(lambda x: 1.5 * x, types_to_apply=INTENSITY),
RandomBiasField(),
Rescale((0, 1)),
ZNormalization(masking_method='label'),
HistogramStandardization(landmarks_dict=landmarks_dict),
RandomElasticDeformation(proportion_to_augment=1),
RandomAffine(),
Pad((1, 2, 3, 0, 5, 6)),
Crop((3, 2, 8, 0, 1, 4)),
)
transformed = self.get_sample()
for transform in transforms:
transformed = transform(transformed)
def __init__(self, transform1, m1, p1, transform2, m2, p2):
ranges = {
'flip': np.zeros(10),
'affine': np.linspace(0, 180, 10),
'noise': np.linspace(0, 0.5, 10),
'blur': np.arange(10),
'elasticD': np.zeros(10)
}
transforms = {
'flip': lambda magnitude, p: RandomFlip(p=p),
'affine':
lambda magnitude, p: RandomAffine(degrees=(magnitude), p=p),
'noise': lambda magnitude, p: RandomNoise(std=magnitude, p=p),
'blur': lambda magnitude, p: RandomBlur(std=magnitude, p=p),
'elasticD': lambda magnitude, p: RandomElasticDeformation(p=p)
}
self.transform1 = transforms[transform1]
self.t1_input = transform1
self.m1 = ranges[transform1][m1]
self.m1_input = m1
self.p1 = p1
self.transform2 = transforms[transform2]
self.t2_input = transform2
self.m2 = ranges[transform2][m2]
self.m2_input = m2
self.p2 = p2
self.kappa = 0.0
def random_augment(x):
'''Randomly augment input data.
Returns: Randomly augmented input
'''
# Data augmentations to be used
transforms_dict = {
RandomFlip(): 1,
RandomElasticDeformation(): 1,
RandomAffine(): 1,
RandomNoise(): 1,
RandomBlur(): 1
}
# Create random transform, with a p chance to apply augmentation
transform = OneOf(transforms_dict, p=0.95)
return augment(x, transform)
def predict_majority(model, x, y):
'''Augments all samples of the original data, and chooses majority predictions predicted by the model.
Usage: predict_majority(model, x_original, y_original)
'''
# Reshape arrays
x = np.reshape(x, (len(x), 40, 40, 4, 1))
y = [x - 1 for x in y]
y = to_categorical(y, 5)
# Predict majority
x_flip = augment(x.copy(), RandomFlip())
x_ed = augment(x.copy(), RandomElasticDeformation())
x_affine = augment(x.copy(), RandomAffine())
x_noise = augment(x.copy(), RandomNoise())
x_blur = augment(x.copy(), RandomBlur())
y_true = pred_list(y)
y_pred = pred_list(model.predict(x.copy()))
y_flip = pred_list(model.predict(x_flip.copy()))
y_ed = pred_list(model.predict(x_ed.copy()))
y_affine = pred_list(model.predict(x_affine.copy()))
y_noise = pred_list(model.predict(x_noise.copy()))
y_blur = pred_list(model.predict(x_blur.copy()))
y_most = []
correct = 0
print(
'\nEntry Number | Prediction (None, Flip, Elastic Deformation, Affine, Noise, Blur) | Actual'
)
for i in range(len(y_true)):
preds = [
y_pred[i], y_flip[i], y_ed[i], y_affine[i], y_noise[i], y_blur[i]
]
most = max(set(preds), key=preds.count)
y_most.append(most)
print('Entry', i, '| Predictions:', preds, '| Most Occuring:', most,
'| Correct:', y_true[i])
if most == y_true[i]:
correct += 1
print('\nTest Accuracy: ', correct / len(y_true))
print('Quadratic Weighted Kappa: ',
cohen_kappa_score(y_true, y_most, weights='quadratic'))
def blur(std, p=1):
return RandomBlur(std=std, p=p)
def compose_transforms() -> Compose:
print(f"{ctime()}: Setting up transformations...")
"""
# Our Preprocessing Options available in TorchIO are:
* Intensity
- NormalizationTransform
- RescaleIntensity
- ZNormalization
- HistogramStandardization
* Spatial
- CropOrPad
- Crop
- Pad
- Resample
- ToCanonical
We should read and experiment with these, but for now will just use a bunch with
the default values.
"""
preprocessors = [
ToCanonical(p=1),
ZNormalization(masking_method=None,
p=1), # alternately, use RescaleIntensity
]
"""
# Our Augmentation Options available in TorchIO are:
* Spatial
- RandomFlip
- RandomAffine
- RandomElasticDeformation
* Intensity
- RandomMotion
- RandomGhosting
- RandomSpike
- RandomBiasField
- RandomBlur
- RandomNoise
- RandomSwap
We should read and experiment with these, but for now will just use a bunch with
the default values.
"""
augments = [
RandomFlip(axes=(0, 1, 2), flip_probability=0.5),
RandomAffine(image_interpolation="linear",
p=0.8), # default, compromise on speed + quality
# this will be most processing intensive, leave out for now, see results
# RandomElasticDeformation(p=1),
RandomMotion(),
RandomSpike(),
RandomBiasField(),
RandomBlur(),
RandomNoise(),
]
transform = Compose(preprocessors + augments)
print(f"{ctime()}: Transformations registered.")
return transform
def define_transform(transform,
p,
blur_std=4,
motion_trans=10,
motion_deg=10,
motion_num=2,
biascoeff=0.5,
noise_std=0.25,
affine_trans=10,
affine_deg=10,
elastic_disp=7.5,
resample_size=1,
target_shape=0):
### (1) try with different blur
if transform == 'blur':
transforms = [RandomBlur(std=(blur_std, blur_std), p=p, seed=None)]
transforms = Compose(transforms)
### (2) try with different motion artifacts
if transform == 'motion':
transforms = [
RandomMotion(degrees=motion_deg,
translation=motion_trans,
num_transforms=motion_num,
image_interpolation=Interpolation.LINEAR,
p=p,
seed=None),
]
transforms = Compose(transforms)
### (3) with random bias fields
if transform == 'biasfield':
transforms = [
RandomBiasField(coefficients=biascoeff, order=3, p=p, seed=None)
]
transforms = Compose(transforms)
### (4) try with different noise artifacts
if transform == 'noise':
transforms = [
RandomNoise(mean=0, std=(noise_std, noise_std), p=p, seed=None)
]
transforms = Compose(transforms)
### (5) try with different warp (affine transformatins)
if transform == 'affine':
transforms = [
RandomAffine(scales=(1, 1),
degrees=(affine_deg),
isotropic=False,
default_pad_value='otsu',
image_interpolation=Interpolation.LINEAR,
p=p,
seed=None)
]
transforms = Compose(transforms)
### (6) try with different warp (elastic transformations)
if transform == 'elastic':
transforms = [
RandomElasticDeformation(num_control_points=elastic_disp,
max_displacement=20,
locked_borders=2,
image_interpolation=Interpolation.LINEAR,
p=p,
seed=None),
]
transforms = Compose(transforms)
if transform == 'resample':
transforms = [
Resample(target=resample_size,
image_interpolation=Interpolation.LINEAR,
p=p),
CropOrPad(target_shape=target_shape, p=1)
]
transforms = Compose(transforms)
return transforms
znormed = transform(sample)
fig, ax = plt.subplots(dpi=100)
plot_histogram(ax, znormed.mri.data, label='Z-normed', alpha=1)
ax.set_title('Intensity values of one sample after z-normalization')
ax.set_xlabel('Intensity')
ax.grid()
training_transform = Compose([
ToCanonical(),
# Resample(4),
CropOrPad((112, 112, 48), padding_mode=0), #reflect , original 112,112,48
RandomMotion(num_transforms=6, image_interpolation='nearest', p=0.2),
HistogramStandardization({'mri': landmarks}),
RandomBiasField(p=0.2),
RandomBlur(p=0.2),
ZNormalization(masking_method=ZNormalization.mean),
RandomFlip(axes=['inferior-superior'], flip_probability=0.2),
# RandomNoise(std=0.5, p=0.2),
RandomGhosting(intensity=1.8, p=0.2),
# RandomNoise(),
# RandomFlip(axes=(0,)),
# OneOf({
# RandomAffine(): 0.8,
# RandomElasticDeformation(): 0.2,
# }),
])
validation_transform = Compose([
ToCanonical(),
# Resample(4),
def main():
opt = parsing_data()
print("[INFO]Reading data")
# Dictionary with data parameters for NiftyNet Reader
if torch.cuda.is_available():
print('[INFO] GPU available.')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
raise Exception(
"[INFO] No GPU found or Wrong gpu id, please run without --cuda")
# FOLDERS
fold_dir = opt.model_dir
fold_dir_model = os.path.join(fold_dir, 'models')
if not os.path.exists(fold_dir_model):
os.makedirs(fold_dir_model)
save_path = os.path.join(fold_dir_model, './CP_{}.pth')
output_path = os.path.join(fold_dir, 'output')
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path = os.path.join(output_path, 'output_{}.nii.gz')
# LOGGING
orig_stdout = sys.stdout
if os.path.exists(os.path.join(fold_dir, 'out.txt')):
compt = 0
while os.path.exists(
os.path.join(fold_dir, 'out_' + str(compt) + '.txt')):
compt += 1
f = open(os.path.join(fold_dir, 'out_' + str(compt) + '.txt'), 'w')
else:
f = open(os.path.join(fold_dir, 'out.txt'), 'w')
sys.stdout = f
# SPLITS
split_path = dict()
split_path['control'] = opt.split_control
split_path['augm_control'] = opt.split_control
split_path['lesion'] = opt.split_lesion
for dataset in DATASETS:
assert os.path.isfile(
split_path[dataset]), f'{dataset}: split not found'
path_file = dict()
path_file['control'] = opt.path_control
path_file['augm_control'] = opt.path_control
path_file['lesion'] = opt.path_lesion
list_split = ['training', 'validation']
paths_dict = dict()
for dataset in DATASETS:
df_split = pd.read_csv(split_path[dataset], header=None)
list_file = dict()
for split in list_split:
list_file[split] = df_split[df_split[1].isin([split])][0].tolist()
paths_dict_dataset = {split: [] for split in list_split}
for split in list_split:
for subject in list_file[split]:
subject_data = []
for modality in MODALITIES[dataset]:
subject_data.append(
Image(
modality, path_file[dataset] + subject + modality +
'.nii.gz', torchio.INTENSITY))
if split in ['training', 'validation']:
subject_data.append(
Image('label',
path_file[dataset] + subject + 'Label.nii.gz',
torchio.LABEL))
paths_dict_dataset[split].append(Subject(*subject_data))
print(dataset, split, len(paths_dict_dataset[split]))
paths_dict[dataset] = paths_dict_dataset
# PREPROCESSING
transform_training = dict()
transform_validation = dict()
for dataset in DATASETS:
if dataset == 'augm_control':
transform_training[dataset] = (
Rescale((0, 1)),
ToCanonical(),
RandomMotion(),
RandomGhosting(),
RandomBiasField(),
RandomBlur((0, 2)),
ZNormalization(),
CenterCropOrPad((144, 192, 144)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomNoise(std_range=(0, 0.10)),
RandomFlip(axes=(0, )),
)
transform_training[dataset] = Compose(transform_training[dataset])
else:
transform_training[dataset] = (
ToCanonical(),
ZNormalization(),
CenterCropOrPad((144, 192, 144)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomNoise(std_range=(0, 0.10)),
RandomFlip(axes=(0, )),
)
transform_training[dataset] = Compose(transform_training[dataset])
transform_validation[dataset] = (
ToCanonical(),
ZNormalization(),
CenterCropOrPad((144, 192, 144)),
)
transform_validation[dataset] = Compose(transform_validation[dataset])
transform = {
'training': transform_training,
'validation': transform_validation
}
# MODEL
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
print("[INFO] Building model")
model = Generic_UNet(input_modalities=['T1', 'all'],
base_num_features=32,
num_classes=nb_classes,
num_pool=4,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
conv_op=torch.nn.Conv3d,
norm_op=torch.nn.InstanceNorm3d,
norm_op_kwargs=norm_op_kwargs,
nonlin=net_nonlin,
nonlin_kwargs=net_nonlin_kwargs,
convolutional_pooling=False,
convolutional_upsampling=False,
final_nonlin=torch.nn.Softmax(1),
input_features={
'T1': 1,
'all': 4
})
print("[INFO] Training")
train(paths_dict, model, transform, device, save_path, opt)
sys.stdout = orig_stdout
f.close()
def blur(parameters):
return RandomBlur(std=parameters["std"], p=parameters["probability"])