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 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)