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from typing import Union, Tuple, Optional, List, Sequence |
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import torch |
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from ....torchio import TypeRangeFloat |
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from ....data.subject import Subject |
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from ....utils import to_tuple |
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from ... import SpatialTransform |
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from .. import RandomTransform |
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from ...preprocessing import Resample |
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class RandomAnisotropy(RandomTransform, SpatialTransform): |
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r"""Downsample an image along an axis and upsample to initial space. |
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This transform simulates an image that has been acquired using anisotropic |
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spacing and resampled back to a more standard spacing. |
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Args: |
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axes: Axis or tuple of axes along which the image will be downsampled. |
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downsampling: Downsampling factor :math:`m \gt 1`. If a tuple |
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:math:`(a, b)` is provided then :math:`m \sim \mathcal{U}(a, b)`. |
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image_interpolation: Image interpolation used to upsample the image back |
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to its initial spacing. Downsampling is performed using nearest |
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neighbor interpolation. See :ref:`Interpolation` for supported |
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interpolation types. |
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p: Probability that this transform will be applied. |
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keys: See :py:class:`~torchio.transforms.Transform`. |
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Example: |
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>>> import torchio as tio |
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>>> transform = tio.RandomAnisotropy(axes=1, downsampling=2.) # Multiply spacing of second axis by 2 |
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>>> transform = tio.RandomAnisotropy( |
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... axes=(0, 1, 2), |
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... downsampling=(2, 5), |
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... ) # Multiply spacing of one of the 3 axes by a factor randomly chosen in [2, 5] |
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>>> colin = tio.datasets.Colin27() |
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>>> transformed = transform(colin) # images have now anisotropic spacing |
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""" |
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def __init__( |
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self, |
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axes: Union[int, Tuple[int, ...]] = (0, 1, 2), |
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downsampling: TypeRangeFloat = (1.5, 5), |
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image_interpolation: str = 'linear', |
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p: float = 1, |
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keys: Optional[Sequence[str]] = None, |
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): |
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super().__init__(p=p, keys=keys) |
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self.axes = self.parse_axes(axes) |
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self.downsampling_range = self.parse_range( |
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downsampling, 'downsampling', min_constraint=1) |
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self.image_interpolation = self.parse_interpolation(image_interpolation) |
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def get_params( |
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self, |
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axes: Tuple[int, ...], |
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downsampling_range: Tuple[float, float], |
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) -> List[bool]: |
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axis = axes[torch.randint(0, len(axes), (1,))] |
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downsampling = self.sample_uniform(*downsampling_range).item() |
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return axis, downsampling |
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@staticmethod |
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def parse_axes(axes: Union[int, Tuple[int, ...]]): |
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axes_tuple = to_tuple(axes) |
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for axis in axes_tuple: |
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is_int = isinstance(axis, int) |
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if not is_int or axis not in (0, 1, 2): |
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raise ValueError('All axes must be 0, 1 or 2') |
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return axes_tuple |
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def apply_transform(self, subject: Subject) -> Subject: |
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axis, downsampling = self.get_params(self.axes, self.downsampling_range) |
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target_spacing = list(subject.spacing) |
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target_spacing[axis] *= downsampling |
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downsample = Resample( |
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tuple(target_spacing), |
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image_interpolation='nearest', |
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) |
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downsampled = downsample(subject) |
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upsample = Resample( |
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subject.get_first_image(), |
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image_interpolation=self.image_interpolation, |
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) |
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upsampled = upsample(downsampled) |
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return upsampled |
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fepegar /
torchio
