def ukbb_sax_transform(self):
train_transform = ts.Compose([
ts.PadNumpy(size=self.scale_size),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
ts.RandomFlip(h=True, v=True, p=self.random_flip_prob),
ts.RandomAffine(rotation_range=self.rotate_val,
translation_range=self.shift_val,
zoom_range=self.scale_val,
interp=('bilinear', 'nearest')),
ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.RandomCrop(size=self.patch_size),
ts.TypeCast(['float', 'long'])
])
valid_transform = ts.Compose([
ts.PadNumpy(size=self.scale_size),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.SpecialCrop(size=self.patch_size, crop_type=0),
ts.TypeCast(['float', 'long'])
])
return {'train': train_transform, 'valid': valid_transform}
def test_3d_sax_transform(self):
test_transform = ts.Compose([
ts.PadFactorNumpy(factor=self.division_factor),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float']),
#ts.NormalizeMedicPercentile(norm_flag=True),
ts.NormalizeMedic(norm_flag=True),
ts.ChannelsLast(),
ts.AddChannel(axis=0),
])
return {'test': test_transform}
def ultrasound_transform(self):
train_transform = ts.Compose([
ts.ToTensor(),
ts.TypeCast(['float']),
ts.AddChannel(axis=0),
ts.SpecialCrop(self.patch_size, 0),
ts.RandomFlip(h=True, v=False, p=self.random_flip_prob),
ts.RandomAffine(rotation_range=self.rotate_val,
translation_range=self.shift_val,
zoom_range=self.scale_val,
interp=('bilinear')),
ts.StdNormalize(),
])
valid_transform = ts.Compose([
ts.ToTensor(),
ts.TypeCast(['float']),
ts.AddChannel(axis=0),
ts.SpecialCrop(self.patch_size, 0),
ts.StdNormalize(),
])
return {'train': train_transform, 'valid': valid_transform}
def hms_sax_transform(self):
# Training transformation
# 2D Stack input - 3D High Resolution output segmentation
train_transform = []
valid_transform = []
# First pad to a fixed size
# Torch tensor
# Channels first
# Joint affine transformation
# In-plane respiratory motion artefacts (translation and rotation)
# Random Crop
# Normalise the intensity range
train_transform = ts.Compose([])
return {'train': train_transform, 'valid': valid_transform}