def get_train_transform(landmarks_path, resection_params=None):
spatial_transform = tio.Compose((
tio.OneOf({
tio.RandomAffine(): 0.9,
tio.RandomElasticDeformation(): 0.1,
}),
tio.RandomFlip(),
))
resolution_transform = tio.OneOf(
(
tio.RandomAnisotropy(),
tio.RandomBlur(),
),
p=0.75,
)
transforms = []
if resection_params is not None:
transforms.append(get_simulation_transform(resection_params))
if landmarks_path is not None:
transforms.append(
tio.HistogramStandardization({'image': landmarks_path}))
transforms.extend([
# tio.RandomGamma(p=0.2),
resolution_transform,
tio.RandomGhosting(p=0.2),
tio.RandomSpike(p=0.2),
tio.RandomMotion(p=0.2),
tio.RandomBiasField(p=0.5),
tio.ZNormalization(masking_method=tio.ZNormalization.mean),
tio.RandomNoise(p=0.75), # always after ZNorm and after blur!
spatial_transform,
get_tight_crop(),
])
return tio.Compose(transforms)
def test_transforms(self):
landmarks_dict = dict(
t1=np.linspace(0, 100, 13),
t2=np.linspace(0, 100, 13),
)
elastic = torchio.RandomElasticDeformation(max_displacement=1)
transforms = (
torchio.CropOrPad((9, 21, 30)),
torchio.ToCanonical(),
torchio.Resample((1, 1.1, 1.25)),
torchio.RandomFlip(axes=(0, 1, 2), flip_probability=1),
torchio.RandomMotion(),
torchio.RandomGhosting(axes=(0, 1, 2)),
torchio.RandomSpike(),
torchio.RandomNoise(),
torchio.RandomBlur(),
torchio.RandomSwap(patch_size=2, num_iterations=5),
torchio.Lambda(lambda x: 2 * x, types_to_apply=torchio.INTENSITY),
torchio.RandomBiasField(),
torchio.RescaleIntensity((0, 1)),
torchio.ZNormalization(masking_method='label'),
torchio.HistogramStandardization(landmarks_dict=landmarks_dict),
elastic,
torchio.RandomAffine(),
torchio.OneOf({
torchio.RandomAffine(): 3,
elastic: 1
}),
torchio.Pad((1, 2, 3, 0, 5, 6), padding_mode='constant', fill=3),
torchio.Crop((3, 2, 8, 0, 1, 4)),
)
transform = torchio.Compose(transforms)
transform(self.sample)
def byol_aug(filename):
"""
BYOL minimizes the distance between representations of each sample and a transformation of that sample.
Examples of transformations include: translation, rotation, blurring, color inversion, color jitter, gaussian noise.
Return an augmented dataset that consisted the above mentioned transformation. Will be used in the training.
"""
image = tio.ScalarImage(filename)
get_foreground = tio.ZNormalization.mean
training_transform = tio.Compose([
tio.CropOrPad((180, 220, 170)), # zero mean, unit variance of foreground
tio.ZNormalization(
masking_method=get_foreground),
tio.RandomBlur(p=0.25), # blur 25% of times
tio.RandomNoise(p=0.25), # Gaussian noise 25% of times
tio.OneOf({ # either
tio.RandomAffine(): 0.8, # random affine
tio.RandomElasticDeformation(): 0.2, # or random elastic deformation
}, p=0.8), # applied to 80% of images
tio.RandomBiasField(p=0.3), # magnetic field inhomogeneity 30% of times
tio.OneOf({ # either
tio.RandomMotion(): 1, # random motion artifact
tio.RandomSpike(): 2, # or spikes
tio.RandomGhosting(): 2, # or ghosts
}, p=0.5), # applied to 50% of images
])
tfs_image = training_transform(image)
return tfs_image
def get_transform(self, channels, is_3d=True, labels=True):
landmarks_dict = {
channel: np.linspace(0, 100, 13)
for channel in channels
}
disp = 1 if is_3d else (1, 1, 0.01)
elastic = tio.RandomElasticDeformation(max_displacement=disp)
cp_args = (9, 21, 30) if is_3d else (21, 30, 1)
resize_args = (10, 20, 30) if is_3d else (10, 20, 1)
flip_axes = axes_downsample = (0, 1, 2) if is_3d else (0, 1)
swap_patch = (2, 3, 4) if is_3d else (3, 4, 1)
pad_args = (1, 2, 3, 0, 5, 6) if is_3d else (0, 0, 3, 0, 5, 6)
crop_args = (3, 2, 8, 0, 1, 4) if is_3d else (0, 0, 8, 0, 1, 4)
remapping = {1: 2, 2: 1, 3: 20, 4: 25}
transforms = [
tio.CropOrPad(cp_args),
tio.EnsureShapeMultiple(2, method='crop'),
tio.Resize(resize_args),
tio.ToCanonical(),
tio.RandomAnisotropy(downsampling=(1.75, 2), axes=axes_downsample),
tio.CopyAffine(channels[0]),
tio.Resample((1, 1.1, 1.25)),
tio.RandomFlip(axes=flip_axes, flip_probability=1),
tio.RandomMotion(),
tio.RandomGhosting(axes=(0, 1, 2)),
tio.RandomSpike(),
tio.RandomNoise(),
tio.RandomBlur(),
tio.RandomSwap(patch_size=swap_patch, num_iterations=5),
tio.Lambda(lambda x: 2 * x, types_to_apply=tio.INTENSITY),
tio.RandomBiasField(),
tio.RescaleIntensity(out_min_max=(0, 1)),
tio.ZNormalization(),
tio.HistogramStandardization(landmarks_dict),
elastic,
tio.RandomAffine(),
tio.OneOf({
tio.RandomAffine(): 3,
elastic: 1,
}),
tio.RemapLabels(remapping=remapping, masking_method='Left'),
tio.RemoveLabels([1, 3]),
tio.SequentialLabels(),
tio.Pad(pad_args, padding_mode=3),
tio.Crop(crop_args),
]
if labels:
transforms.append(tio.RandomLabelsToImage(label_key='label'))
return tio.Compose(transforms)
def get_context(device, variables, augmentation_mode, **kwargs):
context = base_config.get_context(device, variables, **kwargs)
context.file_paths.append(os.path.abspath(__file__))
context.config.update({'augmentation_mode': augmentation_mode})
# training_transform is a tio.Compose where the second transform is the augmentation
dataset_defn = context.get_component_definition("dataset")
training_transform = dataset_defn['params']['transforms']['training']
dwi_augmentation = ReconstructMeanDWI(num_dwis=(1, 7),
num_directions=(1, 3),
directionality=(4, 10))
noise = tio.RandomNoise(std=0.035, p=0.3)
blur = tio.RandomBlur((0, 1), p=0.2)
standard_augmentations = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomElasticDeformation(p=0.5,
num_control_points=(7, 7, 4),
locked_borders=1,
image_interpolation='bspline',
exclude="full_dwi"),
tio.RandomBiasField(p=0.5),
tio.RescaleIntensity((0, 1), (0.01, 99.9)),
tio.RandomGamma(p=0.8),
tio.RescaleIntensity((-1, 1)),
tio.OneOf([
tio.Compose([blur, noise]),
tio.Compose([noise, blur]),
])
],
exclude="full_dwi")
if augmentation_mode == 'no_augmentation':
training_transform.transforms.pop(1)
elif augmentation_mode == 'standard':
training_transform.transforms[1] = standard_augmentations
elif augmentation_mode == 'dwi_reconstruction':
training_transform.transforms[1] = dwi_augmentation
elif augmentation_mode == 'combined':
training_transform.transforms[1] = tio.Compose(
[dwi_augmentation, standard_augmentations])
else:
raise ValueError(f"Invalid augmentation mode {augmentation_mode}")
return context
def get_transform(self, channels, is_3d=True, labels=True):
landmarks_dict = {
channel: np.linspace(0, 100, 13)
for channel in channels
}
disp = 1 if is_3d else (1, 1, 0.01)
elastic = torchio.RandomElasticDeformation(max_displacement=disp)
cp_args = (9, 21, 30) if is_3d else (21, 30, 1)
flip_axes = axes_downsample = (0, 1, 2) if is_3d else (0, 1)
swap_patch = (2, 3, 4) if is_3d else (3, 4, 1)
pad_args = (1, 2, 3, 0, 5, 6) if is_3d else (0, 0, 3, 0, 5, 6)
crop_args = (3, 2, 8, 0, 1, 4) if is_3d else (0, 0, 8, 0, 1, 4)
transforms = [
torchio.CropOrPad(cp_args),
torchio.ToCanonical(),
torchio.RandomDownsample(downsampling=(1.75, 2),
axes=axes_downsample),
torchio.Resample((1, 1.1, 1.25)),
torchio.RandomFlip(axes=flip_axes, flip_probability=1),
torchio.RandomMotion(),
torchio.RandomGhosting(axes=(0, 1, 2)),
torchio.RandomSpike(),
torchio.RandomNoise(),
torchio.RandomBlur(),
torchio.RandomSwap(patch_size=swap_patch, num_iterations=5),
torchio.Lambda(lambda x: 2 * x, types_to_apply=torchio.INTENSITY),
torchio.RandomBiasField(),
torchio.RescaleIntensity((0, 1)),
torchio.ZNormalization(),
torchio.HistogramStandardization(landmarks_dict),
elastic,
torchio.RandomAffine(),
torchio.OneOf({
torchio.RandomAffine(): 3,
elastic: 1,
}),
torchio.Pad(pad_args, padding_mode=3),
torchio.Crop(crop_args),
]
if labels:
transforms.append(torchio.RandomLabelsToImage(label_key='label'))
return torchio.Compose(transforms)
def test_non_invertible(self):
transform = tio.RandomBlur()
with self.assertRaises(RuntimeError):
transform.inverse()
lr=tio.ScalarImage(image_file),
)
#normalization = tio.ZNormalization(masking_method='label')#masking_method=tio.ZNormalization.mean)
normalization = tio.ZNormalization()
onehot = tio.OneHot()
spatial = tio.RandomAffine(scales=0.1, degrees=10, translation=0, p=0)
bias = tio.RandomBiasField(coefficients=0.5, p=0)
flip = tio.RandomFlip(axes=('LR', ), p=1)
noise = tio.RandomNoise(std=0.1, p=0)
sampling_1mm = tio.Resample(1)
sampling_05mm = tio.Resample(0.5)
blur = tio.RandomBlur(0.5)
sampling_jess = tio.Resample((0.8, 0.8, 2), exclude='hr')
blur_jess = tio.Blur(std=(0.001, 0.001, 1), exclude='hr')
downsampling_jess = tio.Resample((0.8, 0.8, 2), exclude='hr')
upsampling_jess = tio.Resample(target='hr', exclude='hr')
tocanonical = tio.ToCanonical()
crop1 = tio.CropOrPad((290, 290, 200))
crop2 = tio.CropOrPad((290, 290, 200), include='lr')
#transforms = tio.Compose([spatial, bias, flip, normalization, noise])
#transforms = tio.Compose([normalization, sampling_1mm, noise, blur, sampling_05mm])
#transforms = tio.Compose([blur_jess,sampling_jess])
transforms = tio.Compose([
"""
Exclude images from transform
=============================
In this example we show how the kwargs ``include`` and ``exclude`` can be
used to apply a transform to only some of the images within a subject.
"""
import torch
import torchio as tio
torch.manual_seed(0)
subject = tio.datasets.Pediatric(years=(4.5, 8.5))
subject.plot()
transform = tio.Compose([
tio.RandomAffine(degrees=(20, 30), exclude='t1'),
tio.RandomBlur(std=(3, 4), include='t2'),
])
transformed = transform(subject)
transformed.plot()
sns.distplot(fpg.mri.data, ax=axes[0], kde=False)
sns.distplot(standardized_foreground.mri.data, ax=axes[1], kde=False)
axes[0].set_title('Original histogram')
axes[1].set_title('Z-normalization using foreground stats')
axes[0].set_ylim((0, 1e6))
axes[1].set_ylim((0, 1e6))
plt.tight_layout()
#The second mode is now closer to zero, as only the foreground voxels have been
#used to compute the statistics.
#Random blur
#We can use RandomBlur to smooth/blur the images. The standard deviations of the
#Gaussian kernels are expressed in mm and will be computed independently for each axis.
blur = tio.RandomBlur(seed=42)
blurred = blur(fpg_ras)
show_fpg(blurred)
#Random noise
#Gaussian noise can be simulated using RandomNoise. This transform is easiest to use after
#ZNormalization, as we know beforehand that the mean and standard deviation of the input will
#be 0 and 1, respectively. If necessary, the noise mean and std can be set using the
#corresponding keyword arguments.
#Noise in MRI is actually Rician, but it is nearly Gaussian for SNR > 2 (i.e. foreground).
add_noise = tio.RandomNoise(std=0.5, seed=42)
standard = standardize(fpg_ras)
noisy = add_noise(standard)
show_fpg(noisy)
def get_context(device, variables, fold=0, **kwargs):
context = TorchContext(device, name="msseg2", variables=variables)
context.file_paths.append(os.path.abspath(__file__))
context.config = config = {'fold': fold, 'patch_size': 96}
input_images = ["flair_time01", "flair_time02"]
subject_loader = ComposeLoaders([
ImageLoader(glob_pattern="flair_time01*",
image_name='flair_time01',
image_constructor=tio.ScalarImage),
ImageLoader(glob_pattern="flair_time02*",
image_name='flair_time02',
image_constructor=tio.ScalarImage),
ImageLoader(glob_pattern="brain_mask.*",
image_name='brain_mask',
image_constructor=tio.LabelMap,
label_values={"brain": 1}),
ImageLoader(glob_pattern="ground_truth.*",
image_name="ground_truth",
image_constructor=tio.LabelMap,
label_values={"lesion": 1}),
])
cohorts = {}
cohorts['all'] = RequireAttributes(input_images)
cohorts['validation'] = RandomFoldFilter(num_folds=5,
selection=fold,
seed=0xDEADBEEF)
cohorts['training'] = NegateFilter(cohorts['validation'])
common_transforms_1 = tio.Compose([
SetDataType(torch.float),
EnforceConsistentAffine(source_image_name='flair_time01'),
TargetResample(target_spacing=1, tolerance=0.11),
CropToMask('brain_mask'),
MinSizePad(config['patch_size'])
])
augmentations = tio.Compose([
RandomPermuteDimensions(),
tio.RandomFlip(axes=(0, 1, 2)),
tio.OneOf(
{
tio.RandomElasticDeformation():
0.2,
tio.RandomAffine(scales=0.2,
degrees=45,
default_pad_value='otsu'):
0.8,
},
p=0.75),
tio.RandomBiasField(p=0.5),
tio.RescaleIntensity((0, 1), (0.01, 99.9)),
tio.RandomGamma(p=0.8),
tio.RescaleIntensity((-1, 1)),
tio.RandomBlur((0, 1), p=0.2),
tio.RandomNoise(std=0.1, p=0.35)
])
common_transforms_2 = tio.Compose([
tio.RescaleIntensity((-1, 1.), (0.05, 99.5)),
ConcatenateImages(image_names=["flair_time01", "flair_time02"],
image_channels=[1, 1],
new_image_name="X"),
RenameProperty(old_name='ground_truth', new_name='y'),
CustomOneHot(include="y"),
])
transforms = {
'default':
tio.Compose([common_transforms_1, common_transforms_2]),
'training':
tio.Compose([
common_transforms_1, augmentations, common_transforms_2,
ImageFromLabels(new_image_name="patch_probability",
label_weights=[('brain_mask', 'brain', 1),
('y', 'lesion', 100)])
]),
}
context.add_component("dataset",
SubjectFolder,
root='$DATASET_PATH',
subject_path="",
subject_loader=subject_loader,
cohorts=cohorts,
transforms=transforms)
context.add_component("model",
ModularUNet,
in_channels=2,
out_channels=2,
filters=[40, 40, 80, 80, 120, 120],
depth=6,
block_params={'residual': True},
downsample_class=BlurConv3d,
downsample_params={
'kernel_size': 3,
'stride': 2,
'padding': 1
},
upsample_class=BlurConvTranspose3d,
upsample_params={
'kernel_size': 3,
'stride': 2,
'padding': 1,
'output_padding': 0
})
context.add_component("optimizer",
SGD,
params="self.model.parameters()",
lr=0.001,
momentum=0.95)
context.add_component("criterion",
HybridLogisticDiceLoss,
logistic_class_weights=[1, 100])
training_evaluators = [
ScheduledEvaluation(evaluator=SegmentationEvaluator(
'y_pred_eval', 'y_eval'),
log_name='training_segmentation_eval',
interval=15),
ScheduledEvaluation(evaluator=LabelMapEvaluator('y_pred_eval'),
log_name='training_label_eval',
interval=15),
ScheduledEvaluation(evaluator=ContourImageEvaluator(
"random",
'flair_time02',
'y_pred_eval',
'y_eval',
slice_id=0,
legend=True,
ncol=2,
interesting_slice=True,
split_subjects=False),
log_name=f"contour_image",
interval=15),
]
validation_evaluators = [
ScheduledEvaluation(evaluator=SegmentationEvaluator(
"y_pred_eval", "y_eval"),
log_name="segmentation_eval",
cohorts=["validation"],
interval=50),
ScheduledEvaluation(evaluator=ContourImageEvaluator(
"interesting",
'flair_time02',
'y_pred_eval',
'y_eval',
slice_id=0,
legend=True,
ncol=1,
interesting_slice=True,
split_subjects=True),
log_name=f"contour_image",
cohorts=["validation"],
interval=50),
]
def scoring_function(evaluation_dict):
# Grab the output of the SegmentationEvaluator
seg_eval = evaluation_dict['segmentation_eval']['validation']
# Take mean dice, while accounting for subjects which have no lesions.
# Dice is 0/0 = nan when the model correctly outputs no lesions. This is counted as a score of 1.0.
# Dice is (>0)/0 = posinf when the model incorrectly predicts lesions when there are none.
# This is counted as a score of 0.0.
dice = torch.tensor(seg_eval["subject_stats"]['dice.lesion'])
dice = dice.nan_to_num(nan=1.0, posinf=0.0)
score = dice.mean()
return score
train_predictor = StandardPredict(image_names=['X', 'y'])
validation_predictor = PatchPredict(patch_batch_size=32,
patch_size=config['patch_size'],
patch_overlap=(config['patch_size'] //
8),
padding_mode=None,
overlap_mode='average',
image_names=['X'])
patch_sampler = tio.WeightedSampler(patch_size=config['patch_size'],
probability_map='patch_probability')
train_dataloader_factory = PatchDataLoader(max_length=100,
samples_per_volume=1,
sampler=patch_sampler)
validation_dataloader_factory = StandardDataLoader(
sampler=SequentialSampler)
context.add_component(
"trainer",
SegmentationTrainer,
training_batch_size=4,
save_rate=100,
scoring_interval=50,
scoring_function=scoring_function,
one_time_evaluators=[],
training_evaluators=training_evaluators,
validation_evaluators=validation_evaluators,
max_iterations_with_no_improvement=2000,
train_predictor=train_predictor,
validation_predictor=validation_predictor,
train_dataloader_factory=train_dataloader_factory,
validation_dataloader_factory=validation_dataloader_factory)
return context
Res 07mm
0dice_lab 0.51 dice_hot 0.19 dice_pv 0.28
dice_lab_GM 1.2 dice_hot_GM 0.49 dice_pv_GM 0.97
dice_lab_WM 0.8 dice_hot_WM 0.16 dice_pv_WM 0.58
dice_lab_CSF 1.4 dice_hot_CSF 0.72 dice_pv_CSF 1.8
dice_lab_both_R_Accu 1.3 dice_hot_both_R_Accu 0.072 dice_pv_both_R_Accu 0.91
dice_lab_both_R_Thal 0.52 dice_hot_both_R_Thal 0.066 dice_pv_both_R_Thal 0.37
"""
#to reproduce, strange bug
import torchio as tio
import copy
sub = tio.datasets.Colin27()
sub.pop('brain')
sub.pop('head')
t = tio.RandomBlur(std=5)
#t = tio.RandomAffine()
label_volume = sub.t1.data > 1000000 #pv.argmax(dim=0, keepdim=True)
label = tio.LabelMap(tensor=label_volume, affine=sub.t1.affine)
#label = tio.ScalarImage(tensor=label_volume, affine=sub.t1.affine)
new_sub = tio.Subject(t1=label)
#new_sub.plot()
tsub = t(new_sub)
dd = copy.deepcopy(tsub.t1.data[0])
dd[:] = 0
# plt.subplot(2, 2, 3) # 当前画在第一行第2列图上
# plt.imshow(image_blur.data[0, 80, :, :])
# plt.figure(1)
# plt.subplot(2, 2, 4) # 当前画在第一行第2列图上
# plt.imshow(image_noisy.data[0, 80, :, :])
# plt.show()
# print(0)
#
# # plt.imshow(one_subject.mri.data[0, 40, :, :])
#
# print(1)
training_transform = tio.Compose(
[
tio.ToCanonical(),
tio.RandomBlur(std=(0, 1), seed=seed, p=0.1), # blur 50% of times
tio.RandomNoise(std=5, seed=1, p=0.5), # Gaussian noise 50% of times
tio.OneOf(
{ # either
tio.RandomAffine(scales=(0.95, 1.05), degrees=5, seed=seed):
0.75, # random affine
tio.RandomElasticDeformation(max_displacement=(5, 5, 5),
seed=seed):
0.25, # or random elastic deformation
},
p=0.8), # applied to 80% of images
])
for one_subject in dataset:
image0 = one_subject.mri
plt.imshow(image0.data[0, int(image0.shape[1] / 2), :, :])
def dataloader(handles, mode = 'train'):
# If pickle exists, load it
try:
with open('../inputs/flpickles/' + mode + '.pickle', 'rb') as f:
images = pickle.load(f)
except:
images = {}
images['Image'] = []
images['Label'] = []
images['Gap'] = []
images['ID'] = []
# Data augmentations
random_flip = tio.RandomFlip(axes=1)
random_flip2 = tio.RandomFlip(axes=2)
random_affine = tio.RandomAffine(seed=0, scales=(3, 3))
random_elastic = tio.RandomElasticDeformation(
max_displacement=(0, 20, 40),
num_control_points=20,
seed=0,
)
rescale = tio.RescaleIntensity((-1, 1), percentiles=(1, 99))
standardize_foreground = tio.ZNormalization(masking_method=lambda x: x > x.mean())
blur = tio.RandomBlur(seed=0)
standardize = tio.ZNormalization()
add_noise = tio.RandomNoise(std=0.5, seed=42)
add_spike = tio.RandomSpike(seed=42)
add_ghosts = tio.RandomGhosting(intensity=1.5, seed=42)
add_motion = tio.RandomMotion(num_transforms=6, image_interpolation='nearest', seed=42)
swap = tio.RandomSwap(patch_size = 7)
# For each image
for idx, row in handles.iterrows():
im_aug = []
lb_aug = []
gap_aug = []
imgs = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32) # change patch shape if necessary
lbs = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32)
gaps = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32)
im = io.imread(row['Image'])
im = im / 255 # Normalization
im = np.expand_dims(im, axis=0)
imgs[0] = im
im_aug.append(imgs)
images['ID'].append(row['ID'])
if mode == 'train':
im_flip1 = random_flip(im)
imgs[0] = im_flip1
im_aug.append(imgs)
im_flip2 = random_flip2(im)
imgs[0] = im_flip2
im_aug.append(imgs)
im_affine = random_affine(im)
imgs[0] = im_affine
im_aug.append(imgs)
im_elastic = random_elastic(im)
imgs[0] = im_elastic
im_aug.append(imgs)
im_rescale = rescale(im)
imgs[0] = im_rescale
im_aug.append(imgs)
im_standard = standardize_foreground(im)
imgs[0] = im_standard
im_aug.append(imgs)
im_blur = blur(im)
imgs[0] = im_blur
im_aug.append(imgs)
im_noisy = add_noise(standardize(im))
imgs[0] = im_noisy
im_aug.append(imgs)
im_spike = add_spike(im)
imgs[0] = im_spike
im_aug.append(imgs)
im_ghost = add_ghosts(im)
imgs[0] = im_ghost
im_aug.append(imgs)
im_motion = add_motion(im)
imgs[0] = im_motion
im_aug.append(imgs)
im_swap = swap(im)
imgs[0] = im_swap
im_aug.append(imgs)
images['Image'].append(np.array(im_aug))
if mode != 'test':
lb = io.imread(row['Label'])
lb = label_converter(lb)
lb = np.expand_dims(lb, axis=0)
lbs[0] = lb
lb_aug.append(lbs)
gap = io.imread(row['Gap'])
gap = np.expand_dims(gap, axis = 0)
gaps[0] = gap
gap_aug.append(gaps)
if mode == 'train':
lb_flip1 = random_flip(lb)
lbs[0] = lb_flip1
lb_aug.append(lbs)
lb_flip2 = random_flip2(lb)
lbs[0] = lb_flip2
lb_aug.append(lbs)
lb_affine = random_affine(lb)
lbs[0] = lb_affine
lb_aug.append(lbs)
lb_elastic = random_elastic(lb)
lbs[0] = lb_elastic
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
gap_flip1 = random_flip(gap)
gaps[0] = gap_flip1
gap_aug.append(gaps)
gap_flip2 = random_flip2(gap)
gaps[0] = gap_flip2
gap_aug.append(gaps)
gap_affine = random_affine(gap)
gaps[0] = gap_affine
gap_aug.append(gaps)
gap_elastic = random_elastic(gap)
gaps[0] = gap_elastic
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
images['Label'].append(np.array(lb_aug))
images['Gap'].append(np.array(gap_aug))
# Save images
with open("../inputs/flpickles/" + mode + '.pickle', 'wb') as f:
pickle.dump(images, f)
with open('../inputs/flpickles/' + mode + '.pickle', 'rb') as f:
images = pickle.load(f)
return images
def data_loader(train_data_folder=None,
validation_data_folder=None,
test_data_folder=None,
debug_data_folder=None,
num_workers=1,
aug_type='aug0'):
if train_data_folder is not None:
train_images_dir = Path(os.path.join(train_data_folder, 'images'))
train_image_paths = sorted(train_images_dir.glob('*.mha'))
train_subjects = []
for image_path in train_image_paths:
subject = tio.Subject(image=tio.ScalarImage(image_path), )
train_subjects.append(subject)
# z,W,H,N=train_subjects[0].image.data.shape
# for i,subject in zip(range(len(train_subjects)), train_subjects):
# train_subjects[i].image.data=train_subjects[i].image.data.reshape(N,z,W,H)
print("Shape of training image before loading is: " +
str(train_subjects[0].image.data.shape))
if aug_type == 'aug0':
training_transform = tio.Compose([])
elif aug_type == 'aug1':
training_transform = tio.Compose([tio.RandomFlip(axes=(0, 1, 2))])
elif aug_type == 'aug2':
training_transform = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomNoise(mean=0, std=0.1),
tio.RandomBlur(std=(2.5, 2.5, 0.0))
])
elif aug_type == 'aug4':
training_transform = tio.Compose([
tio.RandomAffine(degrees=0,
scales=(0.15, 0.15, 0),
translation=(40, 40, 0),
default_pad_value='minimum',
image_interpolation='linear'),
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomNoise(mean=0, std=0.1),
tio.RandomBlur(std=(2.5, 2.5, 0.0))
])
elif aug_type == 'aug5':
training_transform = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomAffine(degrees=(0, 0, 0, 0, -10, 10),
scales=0,
translation=0,
center='image',
default_pad_value='minimum',
image_interpolation='linear')
])
train_set = tio.SubjectsDataset(train_subjects,
transform=training_transform)
# # Plotting the first patient for inspection
# print("Plotting first subject from the train set...")
# Single_Subject = train_set[0]
# Single_Subject.plot()
print('Training set:', len(train_set), 'subjects')
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=num_workers)
else:
train_loader = None
if validation_data_folder is not None:
validation_images_dir = Path(
os.path.join(validation_data_folder, 'images'))
validation_image_paths = sorted(validation_images_dir.glob('*.mha'))
validation_subjects = []
for image_path in validation_image_paths:
subject = tio.Subject(image=tio.ScalarImage(image_path), )
validation_subjects.append(subject)
validation_transform = tio.Compose([])
validation_set = tio.SubjectsDataset(validation_subjects,
transform=validation_transform)
print('Validation set:', len(validation_set), 'subjects')
validation_loader = torch.utils.data.DataLoader(
validation_set, batch_size=1, num_workers=num_workers)
else:
validation_loader = None
if test_data_folder is not None:
test_images_dir = Path(os.path.join(test_data_folder, 'images'))
test_image_paths = sorted(test_images_dir.glob('*.mha'))
test_subjects = []
for image_path in test_image_paths:
subject = tio.Subject(image=tio.ScalarImage(image_path), )
test_subjects.append(subject)
test_transform = tio.Compose([])
test_set = tio.SubjectsDataset(test_subjects, transform=test_transform)
print('Test set:', len(test_set), 'subjects')
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
num_workers=num_workers)
else:
test_loader = None
if debug_data_folder is not None:
debug_images_dir = Path(os.path.join(debug_data_folder, 'images'))
debug_image_paths = [sorted(debug_images_dir.glob('*.mha'))[0]]
debug_subjects = []
for image_path in debug_image_paths:
subject = tio.Subject(image=tio.ScalarImage(image_path), )
debug_subjects.append(subject)
# z,W,H,N=debug_subjects[0].image.data.shape
# for i,subject in zip(range(len(debug_subjects)), debug_subjects):
# debug_subjects[i].image.data=debug_subjects[i].image.data.reshape(N,z,H,W)
print("Shape of debug image before loading is: " +
str(debug_subjects[0].image.data.shape))
if aug_type == 'aug0':
debug_transform = tio.Compose([])
elif aug_type == 'aug1':
debug_transform = tio.Compose([tio.RandomFlip(axes=(0, 1, 2))])
elif aug_type == 'aug2':
debug_transform = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomNoise(mean=0, std=0.1),
tio.RandomBlur(std=(2.5, 2.5, 0.0))
])
elif aug_type == 'aug4':
debug_transform = tio.Compose([
tio.RandomAffine(degrees=0,
scales=(0.15, 0.15, 0),
translation=(40, 40, 0),
default_pad_value='minimum',
image_interpolation='linear'),
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomNoise(mean=0, std=0.1),
tio.RandomBlur(std=(2.5, 2.5, 0.0))
])
elif aug_type == 'aug5':
debug_transform = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomAffine(degrees=(0, 0, 0, 0, -10, 10),
scales=0,
translation=0,
center='image',
default_pad_value='minimum',
image_interpolation='linear')
])
debug_set = tio.SubjectsDataset(debug_subjects,
transform=debug_transform)
# Plotting the first patient for inspection
# print("Plotting first subject from the debug set...")
# Single_Subject = debug_set[0]
# Single_Subject.plot()
print('Debug set:', len(debug_set), 'subjects')
debug_loader = torch.utils.data.DataLoader(debug_set,
batch_size=1,
num_workers=num_workers)
else:
debug_loader = None
return train_loader, validation_loader, test_loader, debug_loader
import numpy as np
import pandas as pd
import torch
import torchio as tio
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torchio import Image
from torchvision.transforms import Compose
transforms_dict = {
tio.RandomAffine(): 0.55,
tio.RandomElasticDeformation(): 0.25
}
transforms_dict2 = {tio.RandomBlur(): 0.25, tio.RandomMotion(): 0.25}
# for aumentation
transform_flip = tio.OneOf(transforms_dict)
class ADNIDataloaderAllData(Dataset):
def __init__(self, df, root_dir, transform):
self.df = df
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, index):
img_path = os.path.join(self.root_dir, self.df.iloc[index, 1])
import pprint
import torch
import torchio as tio
import matplotlib.pyplot as plt
torch.manual_seed(0)
batch_size = 4
subject = tio.datasets.FPG()
subject.remove_image('seg')
subjects = 4 * [subject]
transform = tio.Compose((
tio.ToCanonical(),
tio.RandomGamma(p=0.75),
tio.RandomBlur(p=0.5),
tio.RandomFlip(),
tio.RescaleIntensity((-1, 1)),
))
dataset = tio.SubjectsDataset(subjects, transform=transform)
transformed = dataset[0]
print('Applied transforms:') # noqa: T001
pprint.pprint(transformed.history) # noqa: T003
print('\nComposed transform to reproduce history:') # noqa: T001
print(transformed.get_composed_history()) # noqa: T001
print('\nComposed transform to invert applied transforms when possible:'
) # noqa: T001, E501
print(transformed.get_inverse_transform(ignore_intensity=False)) # noqa: T001
def get_context(
device,
variables,
fold=0,
predict_hbt=False,
training_batch_size=4,
):
context = TorchContext(device, name="dmri-hippo", variables=variables)
context.file_paths.append(os.path.abspath(__file__))
context.config.update({'fold': fold})
input_images = ["mean_dwi", "md", "fa"]
output_labels = ["whole_roi", "hbt_roi"]
subject_loader = ComposeLoaders([
ImageLoader(glob_pattern="mean_dwi.*",
image_name='mean_dwi',
image_constructor=tio.ScalarImage),
ImageLoader(glob_pattern="md.*",
image_name='md',
image_constructor=tio.ScalarImage),
ImageLoader(glob_pattern="fa.*",
image_name='fa',
image_constructor=tio.ScalarImage),
# ImageLoader(glob_pattern="full_dwi.*", image_name='full_dwi', image_constructor=tio.ScalarImage),
# TensorLoader(glob_pattern="full_dwi_grad.b", tensor_name="grad", belongs_to="full_dwi"),
ImageLoader(glob_pattern="whole_roi.*",
image_name="whole_roi",
image_constructor=tio.LabelMap,
label_values={
"left_whole": 1,
"right_whole": 2
}),
ImageLoader(glob_pattern="whole_roi_alt.*",
image_name="whole_roi_alt",
image_constructor=tio.LabelMap,
label_values={
"left_whole": 1,
"right_whole": 2
}),
ImageLoader(glob_pattern="hbt_roi.*",
image_name="hbt_roi",
image_constructor=tio.LabelMap,
label_values={
"left_head": 1,
"left_body": 2,
"left_tail": 3,
"right_head": 4,
"right_body": 5,
"right_tail": 6
}),
ImageLoader(glob_pattern="../../atlas/whole_roi_union.*",
image_name="whole_roi_union",
image_constructor=tio.LabelMap,
uniform=True),
AttributeLoader(glob_pattern='attributes.*'),
AttributeLoader(
glob_pattern='../../attributes/cross_validation_split.json',
multi_subject=True,
uniform=True),
AttributeLoader(
glob_pattern='../../attributes/ab300_validation_subjects.json',
multi_subject=True,
uniform=True),
AttributeLoader(
glob_pattern='../../attributes/cbbrain_test_subjects.json',
multi_subject=True,
uniform=True),
])
cohorts = {}
cohorts['all'] = RequireAttributes(input_images)
cohorts['cross_validation'] = RequireAttributes(['fold'])
cohorts['training'] = ComposeFilters(
[cohorts['cross_validation'],
ForbidAttributes({"fold": fold})])
cohorts['cbbrain_validation'] = ComposeFilters(
[cohorts['cross_validation'],
RequireAttributes({"fold": fold})])
cohorts['cbbrain_test'] = RequireAttributes({'cbbrain_test': True})
cohorts['ab300_validation'] = RequireAttributes({'ab300_validation': True})
cohorts['ab300_validation_plot'] = ComposeFilters(
[cohorts['ab300_validation'],
RandomSelectFilter(num_subjects=20)])
cohorts['cbbrain'] = RequireAttributes({"protocol": "cbbrain"})
cohorts['ab300'] = RequireAttributes({"protocol": "ab300"})
cohorts['rescans'] = ForbidAttributes({"rescan_id": "None"})
cohorts['fasd'] = RequireAttributes({"pathologies": "FASD"})
cohorts['inter_rater'] = RequireAttributes(["whole_roi_alt"])
common_transforms_1 = tio.Compose([
tio.CropOrPad((96, 88, 24),
padding_mode='minimum',
mask_name='whole_roi_union'),
CustomRemapLabels(remapping=[("right_whole", 2, 1)],
masking_method="Right",
include=["whole_roi"]),
CustomRemapLabels(remapping=[("right_head", 4, 1),
("right_body", 5, 2),
("right_tail", 6, 3)],
masking_method="Right",
include=["hbt_roi"]),
])
noise = tio.RandomNoise(std=0.035, p=0.3)
blur = tio.RandomBlur((0, 1), p=0.2)
standard_augmentations = tio.Compose([
tio.RandomFlip(axes=(0, 1, 2)),
tio.RandomElasticDeformation(p=0.5,
num_control_points=(7, 7, 4),
locked_borders=1,
image_interpolation='bspline',
exclude=["full_dwi"]),
tio.RandomBiasField(p=0.5),
tio.RescaleIntensity((0, 1), (0.01, 99.9)),
tio.RandomGamma(p=0.8),
tio.RescaleIntensity((-1, 1)),
tio.OneOf([
tio.Compose([blur, noise]),
tio.Compose([noise, blur]),
])
],
exclude="full_dwi")
common_transforms_2 = tio.Compose([
tio.RescaleIntensity((-1., 1.), (0.5, 99.5)),
ConcatenateImages(image_names=["mean_dwi", "md", "fa"],
image_channels=[1, 1, 1],
new_image_name="X"),
RenameProperty(old_name="hbt_roi" if predict_hbt else "whole_roi",
new_name="y"),
CustomOneHot(include=["y"])
])
transforms = {
'default':
tio.Compose([common_transforms_1, common_transforms_2]),
'training':
tio.Compose(
[common_transforms_1, standard_augmentations,
common_transforms_2]),
}
context.add_component("dataset",
SubjectFolder,
root='$DATASET_PATH',
subject_path="subjects",
subject_loader=subject_loader,
cohorts=cohorts,
transforms=transforms)
context.add_component("model",
NestedResUNet,
input_channels=3,
output_channels=4 if predict_hbt else 2,
filters=40,
dropout_p=0.2)
context.add_component("optimizer",
Adam,
params="self.model.parameters()",
lr=0.0002)
context.add_component("criterion", HybridLogisticDiceLoss)
training_evaluators = [
ScheduledEvaluation(evaluator=SegmentationEvaluator(
'y_pred_eval', 'y_eval'),
log_name='training_segmentation_eval',
interval=10),
ScheduledEvaluation(evaluator=ContourImageEvaluator(
"Axial",
'mean_dwi',
'y_pred_eval',
'y_eval',
slice_id=12,
legend=True,
ncol=2,
split_subjects=False),
log_name=f"contour_image_training",
interval=50),
]
curve_params = {
"left_whole":
np.array([-1.96312119e-01, 9.46668029e+00, 2.33635173e+03]),
"right_whole":
np.array([-2.68467331e-01, 1.67925603e+01, 2.07224236e+03])
}
validation_evaluators = [
ScheduledEvaluation(evaluator=LabelMapEvaluator(
'y_pred_eval',
curve_params=curve_params,
curve_attribute='age',
stats_to_output=('volume', 'error', 'absolute_error',
'squared_error', 'percent_diff')),
log_name="predicted_label_eval",
cohorts=['cbbrain_validation', 'ab300_validation'],
interval=50),
ScheduledEvaluation(evaluator=SegmentationEvaluator(
"y_pred_eval", "y_eval"),
log_name="segmentation_eval",
cohorts=['cbbrain_validation'],
interval=50),
ScheduledEvaluation(
evaluator=ContourImageEvaluator("Axial",
"mean_dwi",
"y_pred_eval",
"y_eval",
slice_id=10,
legend=True,
ncol=5,
split_subjects=False),
log_name="contour_image_axial",
cohorts=['cbbrain_validation', 'ab300_validation_plot'],
interval=250),
ScheduledEvaluation(
evaluator=ContourImageEvaluator("Coronal",
"mean_dwi",
"y_pred_eval",
"y_eval",
slice_id=44,
legend=True,
ncol=2,
split_subjects=False),
log_name="contour_image_coronal",
cohorts=['cbbrain_validation', 'ab300_validation_plot'],
interval=250),
]
def scoring_function(evaluation_dict):
# Grab the output of the SegmentationEvaluator
seg_eval_cbbrain = evaluation_dict['segmentation_eval'][
'cbbrain_validation']["summary_stats"]
# Get the mean dice for each label (the mean is across subjects)
cbbrain_dice = seg_eval_cbbrain['mean', :, 'dice']
# Now take the mean across all labels
cbbrain_dice = cbbrain_dice.mean()
score = cbbrain_dice
return score
train_predictor = StandardPredict(sagittal_split=True,
image_names=['X', 'y'])
validation_predictor = StandardPredict(sagittal_split=True,
image_names=['X'])
train_dataloader_factory = StandardDataLoader(sampler=RandomSampler)
validation_dataloader_factory = StandardDataLoader(
sampler=SequentialSampler)
context.add_component(
"trainer",
SegmentationTrainer,
training_batch_size=training_batch_size,
save_rate=100,
scoring_interval=50,
scoring_function=scoring_function,
one_time_evaluators=[],
training_evaluators=training_evaluators,
validation_evaluators=validation_evaluators,
max_iterations_with_no_improvement=2000,
train_predictor=train_predictor,
validation_predictor=validation_predictor,
train_dataloader_factory=train_dataloader_factory,
validation_dataloader_factory=validation_dataloader_factory)
return context
