Source code for torchio.transforms.augmentation.spatial.random_anisotropy

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