torchio.transforms.preprocessing.spatial.copy_affine

Complexity

Total Complexity

5

Size/Duplication

Total Lines	80
Duplicated Lines	0 %

Importance

Changes

0

2 Methods

Rating	Name	Duplication	Size	Complexity
A	CopyAffine.apply_transform()	0	8	3
A	CopyAffine.__init__()	0	8	2

import copy
from ....data.subject import Subject
from ... import SpatialTransform


class CopyAffine(SpatialTransform):
    """Copy the spatial metadata from a reference image in the subject.

    Small unexpected differences in spatial metadata across different images
    of a subject can arise due to rounding errors while converting formats.

    If the ``shape`` and ``orientation`` of the images are the same and their
    ``affine`` attributes are different but very similar, this transform can be
    used to avoid errors during safety checks in other transforms and samplers.

    Args:
        target: Name of the image within the subject whose affine matrix will
            be used.

    Example:
        >>> import torch
        >>> import torchio as tio
        >>> import numpy as np
        >>> np.random.seed(0)
        >>> affine = np.diag((*(np.random.rand(3) + 0.5), 1))
        >>> t1 = tio.ScalarImage(tensor=torch.rand(1, 100, 100, 100), affine=affine)
        >>> # Let's simulate a loss of precision
        >>> # (caused for example by NIfTI storing spatial metadata in single precision)
        >>> bad_affine = affine.astype(np.float16)
        >>> t2 = tio.ScalarImage(tensor=torch.rand(1, 100, 100, 100), affine=bad_affine)
        >>> subject = tio.Subject(t1=t1, t2=t2)
        >>> resample = tio.Resample(0.5)
        >>> resample(subject).shape  # error as images are in different spaces
        Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
        File "/Users/fernando/git/torchio/torchio/data/subject.py", line 101, in shape
            self.check_consistent_attribute('shape')
        File "/Users/fernando/git/torchio/torchio/data/subject.py", line 229, in check_consistent_attribute
            raise RuntimeError(message)
        RuntimeError: More than one shape found in subject images:
        {'t1': (1, 210, 244, 221), 't2': (1, 210, 243, 221)}
        >>> transform = tio.CopyAffine('t1')
        >>> fixed = transform(subject)
        >>> resample(fixed).shape
        (1, 210, 244, 221)


    .. warning:: This transform should be used with caution. Modifying the
        spatial metadata of an image manually can lead to incorrect processing
        of the position of anatomical structures. For example, a machine
        learning algorithm might incorrectly predict that a lesion on the right
        lung is on the left lung.

    .. note:: For more information, see some related discussions on GitHub:

        * https://github.com/fepegar/torchio/issues/354
        * https://github.com/fepegar/torchio/discussions/489
        * https://github.com/fepegar/torchio/pull/584
        * https://github.com/fepegar/torchio/issues/430
        * https://github.com/fepegar/torchio/issues/382
        * https://github.com/fepegar/torchio/pull/592
    """  # noqa: E501
    def __init__(self, target: str, **kwargs):
        super().__init__(**kwargs)
        if not isinstance(target, str):
            message = (
                f'The target must be a string, but "{type(target)}" was found'
            )
            raise ValueError(message)
        self.target = target

    def apply_transform(self, subject: Subject) -> Subject:
        if self.target not in subject:
            message = f'Target image "{self.target}" not found in subject'
            raise RuntimeError(message)
        affine = subject[self.target].affine
        for image in self.get_images(subject):
            image.affine = copy.deepcopy(affine)
        return subject