import warnings
from typing import Union, Tuple, List
import torch
from ....typing import TypeRangeFloat
from ....data.subject import Subject
from ....utils import to_tuple
from .. import RandomTransform
from ...preprocessing import Resample
[docs]class RandomAnisotropy(RandomTransform):
r"""Downsample an image along an axis and upsample to initial space.
This transform simulates an image that has been acquired using anisotropic
spacing and resampled back to its original spacing.
Similar to the work by Billot et al.: `Partial Volume Segmentation of Brain
MRI Scans of any Resolution and
Contrast <billot>`_.
Args:
axes: Axis or tuple of axes along which the image will be downsampled.
downsampling: Downsampling factor :math:`m \gt 1`. If a tuple
:math:`(a, b)` is provided then :math:`m \sim \mathcal{U}(a, b)`.
image_interpolation: Image interpolation used to upsample the image
back to its initial spacing. Downsampling is performed using
nearest neighbor interpolation. See :ref:`Interpolation` for
supported interpolation types.
scalars_only: Apply only to instances of :class:`torchio.ScalarImage`.
This is useful when the segmentation quality needs to be kept,
as in `Billot et al. <billot>`_.
**kwargs: See :class:`~torchio.transforms.Transform` for additional
keyword arguments.
.. _billot: https://link.springer.com/chapter/10.1007/978-3-030-59728-3_18
Example:
>>> import torchio as tio
>>> transform = tio.RandomAnisotropy(axes=1, downsampling=2)
>>> transform = tio.RandomAnisotropy(
... axes=(0, 1, 2),
... downsampling=(2, 5),
... ) # Multiply spacing of one of the 3 axes by a factor randomly chosen in [2, 5]
>>> colin = tio.datasets.Colin27()
>>> transformed = transform(colin)
""" # noqa: E501
def __init__(
self,
axes: Union[int, Tuple[int, ...]] = (0, 1, 2),
downsampling: TypeRangeFloat = (1.5, 5),
image_interpolation: str = 'linear',
scalars_only: bool = True,
**kwargs
):
super().__init__(**kwargs)
self.axes = self.parse_axes(axes)
self.downsampling_range = self._parse_range(
downsampling, 'downsampling', min_constraint=1)
parsed_interpolation = self.parse_interpolation(image_interpolation)
self.image_interpolation = parsed_interpolation
self.scalars_only = scalars_only
def get_params(
self,
axes: Tuple[int, ...],
downsampling_range: Tuple[float, float],
) -> List[bool]:
axis = axes[torch.randint(0, len(axes), (1,))]
downsampling = self.sample_uniform(*downsampling_range).item()
return axis, downsampling
@staticmethod
def parse_axes(axes: Union[int, Tuple[int, ...]]):
axes_tuple = to_tuple(axes)
for axis in axes_tuple:
is_int = isinstance(axis, int)
if not is_int or axis not in (0, 1, 2):
raise ValueError('All axes must be 0, 1 or 2')
return axes_tuple
def apply_transform(self, subject: Subject) -> Subject:
is_2d = subject.get_first_image().is_2d()
if is_2d and 2 in self.axes:
warnings.warn(
f'Input image is 2D, but "2" is in axes: {self.axes}',
RuntimeWarning,
)
self.axes = list(self.axes)
self.axes.remove(2)
axis, downsampling = self.get_params(
self.axes,
self.downsampling_range,
)
target_spacing = list(subject.spacing)
target_spacing[axis] *= downsampling
arguments = {
'image_interpolation': 'nearest',
'scalars_only': self.scalars_only,
}
downsample = Resample(
tuple(target_spacing),
**self.add_include_exclude(arguments)
)
downsampled = downsample(subject)
upsample = Resample(
subject.get_first_image(),
image_interpolation=self.image_interpolation,
scalars_only=self.scalars_only,
)
upsampled = upsample(downsampled)
return upsampled