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RemoveLabels

RemoveLabels

Bases: RemapLabels

Remove labels from a label map.

The removed labels are remapped to the background label.

This transformation is not invertible .

Parameters:

Name Type Description Default
labels Sequence[int]

A sequence of label integers that will be removed.

 required
background_label int

integer that specifies which label is considered to be background (typically, 0).

 0
masking_method TypeMaskingMethod

See RemapLabels.

 None
**kwargs

See Transform for additional keyword arguments.

 {}

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

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

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

__call__(data: dict[str, object]) -> dict[str, object]

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

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.

plot

Source code 
import torchio as tio
colin = tio.datasets.Colin27(2008)
label_map = colin.cls
colin.remove_image('t2')
colin.remove_image('pd')
names_to_remove = (
    'Fat',
    'Muscles',
    'Skin and Muscles',
    'Skull',
    'Fat 2',
    'Dura',
    'Marrow'
)
labels = [colin.NAME_TO_LABEL[name] for name in names_to_remove]
skull_stripping = tio.RemoveLabels(labels)
only_brain = skull_stripping(label_map)
colin.add_image(only_brain, 'brain')
colors = {
    0: (0, 0, 0),
    1: (127, 255, 212),
    2: (96, 204, 96),
    3: (240, 230, 140),
    4: (176, 48, 96),
    5: (48, 176, 96),
    6: (220, 247, 164),
    7: (103, 255, 255),
    9: (205, 62, 78),
    10: (238, 186, 243),
    11: (119, 159, 176),
    12: (220, 216, 20),
}
colin.plot(cmap_dict={'cls': colors, 'brain': colors})