siibra.features.image

Multimodal data features in volume or image formats.

Submodules

• siibra.features.image.image
• siibra.features.image.sections
• siibra.features.image.volume_of_interest

Classes

BigBrain1MicronPatch	Base class for anatomically anchored data features.
BlockfaceVolumeOfInterest	Base class for anatomically anchored data features.
CellBodyStainedVolumeOfInterest	Base class for anatomically anchored data features.
CellbodyStainedSection	Base class for anatomically anchored data features.
DTIVolumeOfInterest	Base class for anatomically anchored data features.
LSFMVolumeOfInterest	Base class for anatomically anchored data features.
MRIVolumeOfInterest	Base class for anatomically anchored data features.
MorphometryVolumeOfInterest	Base class for anatomically anchored data features.
PLIVolumeOfInterest	Base class for anatomically anchored data features.
XPCTVolumeOfInterest	Base class for anatomically anchored data features.

Package Contents

class siibra.features.image.BigBrain1MicronPatch(patch: siibra.locations.AxisAlignedPatch, profile: siibra.locations.Contour, section: CellbodyStainedSection, vertex: int, relevance: float, anchor: siibra.features.anchor.AnatomicalAnchor)

Base class for anatomically anchored data features.

__repr__()

fetch(flip: bool = False, resolution_mm: float = -1, **kwargs)

Fetch a volumetric or surface representation from one of the providers.

Parameters:

• format (str, default=None) –

  Requested format. If None, the first supported format matching in self.formats is tried, starting with volumetric formats. It can be explicitly specified as:

  • ’surface’ or ‘mesh’ to fetch a surface format

  • ’volumetric’ or ‘voxel’ to fetch a volumetric format

  • supported format types, see SUPPORTED_FORMATS. This includes

  ’nii’, ‘zip/nii’, ‘neuroglancer/precomputed’, ‘gii-mesh’, ‘neuroglancer/precompmesh’, ‘gii-label’

• **kwargs –

  • resolution_mm: resolution in millimeters as float or a tuple of floats

  • format: the format of the volume, like “mesh” or “nii”

  • voi: a BoundingBox of interest

Returns:

An image (Nifti1Image) or mesh (Dict[‘verts’

Return type:

ndarray, ‘faces’: ndarray, ‘labels’: ndarray])

get_boundingbox(**fetch_kwargs)

Enforce that the bounding box spans the full section thickness.

property bigbrain_section

property profile: siibra.locations.Contour

relevance

property section: CellbodyStainedSection

vertex

class siibra.features.image.BlockfaceVolumeOfInterest(**kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.CellBodyStainedVolumeOfInterest(**kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.CellbodyStainedSection(**kwargs)

Base class for anatomically anchored data features.

get_boundingbox(clip=False, background=0, **fetch_kwargs)

Obtain the bounding box in physical coordinates of this volume.

Parameters:

• clip (bool, default: True) – Whether to clip the background of the volume.

• background (float, default: 0.0) – The background value to clip. .. note:: To use it, clip must be True.

• fetch_kwargs – key word arguments that are used for fetching volumes, such as voi or resolution_mm. Currently, only possible for Neuroglancer volumes except for format.

Raises:

RuntimeError – If the volume provider does not have a bounding box calculator.

class siibra.features.image.DTIVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.LSFMVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.MRIVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.MorphometryVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.PLIVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.

class siibra.features.image.XPCTVolumeOfInterest(modality, **kwargs)

Base class for anatomically anchored data features.