Image

The Image class, representing one medical image, stores a 4D tensor, whose voxels encode, e.g., signal intensity or segmentation labels, and the corresponding affine transform, typically a rigid (Euclidean) transform, to convert voxel indices to world coordinates in mm. Arbitrary fields such as acquisition parameters may also be stored.

Subclasses are used to indicate specific types of images, such as ScalarImage and LabelMap, which are used to store, e.g., CT scans and segmentations, respectively.

An instance of Image can be created using a filepath, a PyTorch tensor, or a NumPy array. This class uses lazy loading, i.e., the data is not loaded from disk at instantiation time. Instead, the data is only loaded when needed for an operation (e.g., if a transform is applied to the image).

The figure below shows two instances of Image. The instance of ScalarImage contains a 4D tensor representing a diffusion MRI, which contains four 3D volumes (one per gradient direction), and the associated affine matrix. Additionally, it stores the strength and direction for each of the four gradients. The instance of LabelMap contains a brain parcellation of the same subject, the associated affine matrix, and the name and color of each brain structure.

Data structures
class torchio.ScalarImage(*args, **kwargs)[source]

Bases: torchio.data.image.Image

Image whose pixel values represent scalars.

Example

>>> import torch
>>> import torchio as tio
>>> # Loading from a file
>>> t1_image = tio.ScalarImage('t1.nii.gz')
>>> dmri = tio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
>>> image = tio.ScalarImage('safe_image.nrrd', check_nans=False)
>>> data, affine = image.data, image.affine
>>> affine.shape
(4, 4)
>>> image.data is image[tio.DATA]
True
>>> image.data is image.tensor
True
>>> type(image.data)
torch.Tensor

See Image for more information.

class torchio.LabelMap(*args, **kwargs)[source]

Bases: torchio.data.image.Image

Image whose pixel values represent categorical labels.

Intensity transforms are not applied to these images.

Example

>>> import torch
>>> import torchio as tio
>>> labels = tio.LabelMap(tensor=torch.rand(1, 128, 128, 68) > 0.5)
>>> labels = tio.LabelMap('t1_seg.nii.gz')  # loading from a file
>>> tpm = tio.LabelMap(                     # loading from files
...     'gray_matter.nii.gz',
...     'white_matter.nii.gz',
...     'csf.nii.gz',
... )

See Image for more information.

class torchio.Image(path: Optional[Union[pathlib.Path, str, Sequence[Union[pathlib.Path, str]]]] = None, type: Optional[str] = None, tensor: Optional[Union[torch.Tensor, numpy.ndarray]] = None, affine: Optional[Union[torch.Tensor, numpy.ndarray]] = None, check_nans: bool = False, channels_last: bool = False, reader: Callable = <function read_image>, **kwargs: Dict[str, Any])[source]

Bases: dict

TorchIO image.

For information about medical image orientation, check out NiBabel docs, the 3D Slicer wiki, Graham Wideman’s website, FSL docs or SimpleITK docs.

Parameters
  • path – Path to a file or sequence of paths to files that can be read by SimpleITK or nibabel, or to a directory containing DICOM files. If tensor is given, the data in path will not be read. If a sequence of paths is given, data will be concatenated on the channel dimension so spatial dimensions must match.

  • type – Type of image, such as torchio.INTENSITY or torchio.LABEL. This will be used by the transforms to decide whether to apply an operation, or which interpolation to use when resampling. For example, preprocessing and augmentation intensity transforms will only be applied to images with type torchio.INTENSITY. Spatial transforms will be applied to all types, and nearest neighbor interpolation is always used to resample images with type torchio.LABEL. The type torchio.SAMPLING_MAP may be used with instances of WeightedSampler.

  • tensor – If path is not given, tensor must be a 4D torch.Tensor or NumPy array with dimensions \((C, W, H, D)\).

  • affine – If path is not given, affine must be a \(4 \times 4\) NumPy array. If None, affine is an identity matrix.

  • check_nans – If True, issues a warning if NaNs are found in the image. If False, images will not be checked for the presence of NaNs.

  • reader – Callable object that takes a path and returns a 4D tensor and a 2D, \(4 \times 4\) affine matrix. This can be used if your data is saved in a custom format, such as .npy (see example below). If the affine matrix is None, an identity matrix will be used.

  • **kwargs – Items that will be added to the image dictionary, e.g. acquisition parameters.

TorchIO images are lazy loaders, i.e. the data is only loaded from disk when needed.

Example

>>> import torchio as tio
>>> import numpy as np
>>> image = tio.ScalarImage('t1.nii.gz')  # subclass of Image
>>> image  # not loaded yet
ScalarImage(path: t1.nii.gz; type: intensity)
>>> times_two = 2 * image.data  # data is loaded and cached here
>>> image
ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
>>> image.save('doubled_image.nii.gz')
>>> numpy_reader = lambda path: np.load(path), np.eye(4)
>>> image = tio.ScalarImage('t1.npy', reader=numpy_reader)
property affine

Affine matrix to transform voxel indices into world coordinates.

as_pil()[source]

Get the image as an instance of PIL.Image.

Note

Values will be clamped to 0-255 and cast to uint8.

Note

To use this method, Pillow needs to be installed: pip install Pillow.

as_sitk(**kwargs)SimpleITK.SimpleITK.Image[source]

Get the image as an instance of sitk.Image.

axis_name_to_index(axis: str)int[source]

Convert an axis name to an axis index.

Parameters

axis – Possible inputs are 'Left', 'Right', 'Anterior', 'Posterior', 'Inferior', 'Superior'. Lower-case versions and first letters are also valid, as only the first letter will be used.

Note

If you are working with animals, you should probably use 'Superior', 'Inferior', 'Anterior' and 'Posterior' for 'Dorsal', 'Ventral', 'Rostral' and 'Caudal', respectively.

Note

If your images are 2D, you can use 'Top', 'Bottom', 'Left' and 'Right'.

property bounds

Position of centers of voxels in smallest and largest coordinates.

property data

Tensor data. Same as Image.tensor.

get_bounds()Tuple[Tuple[float, float], Tuple[float, float], Tuple[float, float]][source]

Get minimum and maximum world coordinates occupied by the image.

get_center(lps: bool = False)Tuple[float, float, float][source]

Get image center in RAS+ or LPS+ coordinates.

Parameters

lps – If True, the coordinates will be in LPS+ orientation, i.e. the first dimension grows towards the left, etc. Otherwise, the coordinates will be in RAS+ orientation.

property height

Image height, if 2D.

property itemsize

Element size of the data type.

load()None[source]

Load the image from disk.

Returns

Tuple containing a 4D tensor of size \((C, W, H, D)\) and a 2D \(4 \times 4\) affine matrix to convert voxel indices to world coordinates.

property memory

Number of Bytes that the tensor takes in the RAM.

numpy()numpy.ndarray[source]

Get a NumPy array containing the image data.

property orientation

Orientation codes.

plot(**kwargs)None[source]

Plot image.

save(path: Union[pathlib.Path, str], squeeze: bool = True)None[source]

Save image to disk.

Parameters
  • path – String or instance of pathlib.Path.

  • squeeze – If True, singleton dimensions will be removed before saving.

set_data(tensor: Union[torch.Tensor, numpy.ndarray])[source]

Store a 4D tensor in the data key and attribute.

Parameters

tensor – 4D tensor with dimensions \((C, W, H, D)\).

property shape

Tensor shape as \((C, W, H, D)\).

property spacing

Voxel spacing in mm.

property spatial_shape

Tensor spatial shape as \((W, H, D)\).

property tensor

Tensor data. Same as Image.data.

property width

Image width, if 2D.