Skip to content

Mask

Bases: IntensityTransform

Set voxels outside a mask to a constant value.

Useful for brain extraction, region-of-interest cropping, or zeroing out background before training.

Parameters:

Name Type Description Default
masking_method str | Callable

Defines the mask. Can be:

  • A str: key to a LabelMap in the subject.
  • A callable: receives the image tensor and returns a boolean mask.
 'brain'
outside_value float

Value to assign to voxels outside the mask.

 0.0
labels list[int] | None

If using a label map, which label values to include in the mask. None means all nonzero values.

 None
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> # Use a brain mask to zero out non-brain voxels
>>> transform = tio.Mask(masking_method="brain")
>>> # Use a callable mask
>>> transform = tio.Mask(masking_method=lambda x: x > 0)
>>> # Keep only specific labels
>>> transform = tio.Mask(masking_method="seg", labels=[1, 2])
Source code in src/torchio/transforms/intensity/mask.py 
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
class Mask(IntensityTransform):
    """Set voxels outside a mask to a constant value.

    Useful for brain extraction, region-of-interest cropping, or
    zeroing out background before training.

    Args:
        masking_method: Defines the mask. Can be:

            - A `str`: key to a [`LabelMap`][torchio.LabelMap] in
              the subject.
            - A callable: receives the image tensor and returns a
              boolean mask.
        outside_value: Value to assign to voxels outside the mask.
        labels: If using a label map, which label values to include
            in the mask. `None` means all nonzero values.
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> # Use a brain mask to zero out non-brain voxels
        >>> transform = tio.Mask(masking_method="brain")
        >>> # Use a callable mask
        >>> transform = tio.Mask(masking_method=lambda x: x > 0)
        >>> # Keep only specific labels
        >>> transform = tio.Mask(masking_method="seg", labels=[1, 2])
    """

    def __init__(
        self,
        *,
        masking_method: str | Callable = "brain",
        outside_value: float = 0.0,
        labels: list[int] | None = None,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.masking_method = masking_method
        self.outside_value = outside_value
        self.labels = labels

    def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
        """No random parameters."""
        return {}

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Apply the mask to each selected image."""
        mask = self._resolve_mask(batch)
        for _name, img_batch in self._get_images(batch).items():
            expanded = mask.expand_as(img_batch.data)
            img_batch.data = torch.where(expanded, img_batch.data, self.outside_value)
        return batch

    def _resolve_mask(self, batch: SubjectsBatch) -> Tensor:
        """Build a boolean mask from the masking method."""
        if callable(self.masking_method) and not isinstance(self.masking_method, str):
            first_img = next(iter(self._get_images(batch).values()))
            return self.masking_method(first_img.data[0]).bool()

        if isinstance(self.masking_method, str):
            key = self.masking_method
            if key not in batch.images:
                msg = (
                    f'Masking method "{key}" not found in batch images.'
                    f" Available: {list(batch.images.keys())}"
                )
                raise KeyError(msg)
            mask_batch = batch.images[key]
            if not issubclass(mask_batch._image_class, LabelMap):
                msg = f'Masking method "{key}" must refer to a LabelMap.'
                raise TypeError(msg)
            mask_data = mask_batch.data[0]
            if self.labels is not None:
                mask = torch.zeros_like(mask_data, dtype=torch.bool)
                for label in self.labels:
                    mask = mask | (mask_data == label)
                return mask
            return mask_data.bool()

        msg = (
            f"masking_method must be a str or callable, got {type(self.masking_method)}"
        )
        raise TypeError(msg)

supports_per_instance_params property

Whether this transform can sample parameters per batch element.

Defaults to False. Transforms that implement per-instance parameter sampling override this to return True. When False, the transform always uses batch-shared parameters regardless of the per_instance flag, preserving the legacy behavior.

supports_per_instance_p property

Whether this transform can gate each batch element independently.

Defaults to False. Shape-preserving transforms that implement per-element probability override this to return True. Shape-changing transforms must leave it False because masked and unmasked elements would have incompatible shapes.

invertible property

Whether this transform can be inverted.

forward(data) 

forward(data: Subject) -> Subject

forward(data: Image) -> Image

forward(data: Tensor) -> Tensor

forward(data: np.ndarray) -> np.ndarray

forward(data: sitk.Image) -> sitk.Image

forward(data: nib.Nifti1Image) -> nib.Nifti1Image

forward(data: dict) -> dict

forward(data: ImagesBatch) -> ImagesBatch

forward(data: SubjectsBatch) -> SubjectsBatch

Apply the transform.

The output type always matches the input type.

Parameters:

Name Type Description Default
data Any

Input data to transform.

 required
Source code in src/torchio/transforms/transform.py 
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
def forward(self, data: Any) -> Any:
    """Apply the transform.

    The output type always matches the input type.

    Args:
        data: Input data to transform.
    """
    if self.copy:
        data = _copy.deepcopy(data)
    batch, unwrap = self._wrap(data)
    # When per-element gating is active, the transform handles the
    # probability itself (masked-out elements get identity params),
    # so skip the batch-wide coin flip here. Apply iff rand < p, so
    # p=0 is always a no-op and p=1 always applies.
    if not self._per_instance_p_active(batch) and torch.rand(1).item() >= self.p:
        return unwrap(batch)
    params = self.make_params(batch)
    batch = self.apply_transform(batch, params)
    # Record history on the batch, unless every element was gated out by
    # per-element probability: that is an exact no-op, and recording it
    # would let history replay (e.g. an invertible spatial transform)
    # trigger an unnecessary identity resample.
    if not _all_elements_gated_out(params):
        trace = AppliedTransform(name=type(self).__name__, params=params)
        if not hasattr(batch, "applied_transforms"):
            batch.applied_transforms = []
        batch.applied_transforms.append(trace)
    result = unwrap(batch)
    # Propagate history to outputs that can carry it
    if (
        hasattr(batch, "applied_transforms")
        and not isinstance(result, (SubjectsBatch, Tensor, np.ndarray))
        and not isinstance(result, dict)
    ):
        with contextlib.suppress(AttributeError):
            result.applied_transforms = list(batch.applied_transforms)
    return result

inverse(params)

Return a transform that undoes this one.

Override in invertible subclasses. The returned transform, when applied, reverses the effect of the forward pass with the given parameters.

Parameters:

Name Type Description Default
params dict[str, Any]

The parameters recorded in the forward pass.

 required

Returns:

Type Description
Transform

A new Transform instance that inverts this one.
Source code in src/torchio/transforms/transform.py 
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
def inverse(self, params: dict[str, Any]) -> Transform:
    """Return a transform that undoes this one.

    Override in invertible subclasses. The returned transform,
    when applied, reverses the effect of the forward pass with
    the given parameters.

    Args:
        params: The parameters recorded in the forward pass.

    Returns:
        A new `Transform` instance that inverts this one.
    """
    msg = f"{type(self).__name__} is not invertible"
    raise NotImplementedError(msg)

to_hydra()

Export as a Hydra-compatible config dict.

Returns a dict with _target_ set to the fully qualified class name and only non-default field values included.

Returns:

Type Description
dict[str, Any]

Dict suitable for hydra.utils.instantiate().
Source code in src/torchio/transforms/transform.py 
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
def to_hydra(self) -> dict[str, Any]:
    """Export as a Hydra-compatible config dict.

    Returns a dict with `_target_` set to the fully qualified
    class name and only non-default field values included.

    Returns:
        Dict suitable for `hydra.utils.instantiate()`.
    """
    from .parameter_range import _ParameterRange

    cls = type(self)
    target = f"torchio.{cls.__qualname__}"
    cfg: dict[str, Any] = {"_target_": target}

    for name, default in _collect_init_params(cls).items():
        value = getattr(self, name, default)
        if isinstance(value, _ParameterRange):
            if value._original == default:
                continue
            value = _hydra_value(value._original)
        elif value == default:
            continue
        else:
            value = _hydra_value(value)
        cfg[name] = value
    return cfg

make_params(batch)

No random parameters.

Source code in src/torchio/transforms/intensity/mask.py 
57
58
59
def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
    """No random parameters."""
    return {}

apply_transform(batch, params)

Apply the mask to each selected image.

Source code in src/torchio/transforms/intensity/mask.py 
61
62
63
64
65
66
67
68
69
70
71
def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Apply the mask to each selected image."""
    mask = self._resolve_mask(batch)
    for _name, img_batch in self._get_images(batch).items():
        expanded = mask.expand_as(img_batch.data)
        img_batch.data = torch.where(expanded, img_batch.data, self.outside_value)
    return batch