Data (pymia.data package)

This data package provides data handling functionality for machine learning (especially deep learning) projects. The concept of the data package is illustrated in the figure below.

The three main components of the data package are creation, extraction, and assembly.

Creation

The creation of a dataset is managed by the Traverser class, which processes the data of every subject (case) iteratively. It employs Load and Callback classes to load the raw data and write it to the dataset. Transform classes can be used to apply modifications to the data, e.g., an intensity normalization. For the ease of usage, the defaults get_default_callbacks() and LoadDefault are implemented, which cover the most fundamental cases. The code example Creation of a dataset illustrates how to create a dataset.

Extraction

Data extraction from the dataset is managed by the PymiaDatasource class, which provides a flexible interface for retrieving data, or chunks of data, to form training samples. An IndexingStrategy is used to define how the data is indexed, meaning accessing, for instance, an image slice or a 3-D patch of an 3-D image. Extractor classes extract the data from the dataset, and Transform classes can be used to alter the extracted data. The code example Data extraction and assembly illustrates how to extract data.

Assembly

The Assembler class manages the assembly of the predicted neural network outputs by using the identical indexing that was employed to extract the data by the PymiaDatasource class. The code example Data extraction and assembly illustrates how to assemble data.

Subpackages

• Backends (pymia.data.backends package)
  • PyTorch
  • TensorFlow
• Creation (pymia.data.creation package)
  • Callback (pymia.data.creation.callback module)
  • File loader (pymia.data.creation.fileloader module)
  • Traverser (pymia.data.creation.traverser module)
  • Writer (pymia.data.creation.writer module)
• Extraction (pymia.data.extraction package)
  • Datasource (pymia.data.extraction.datasource module)
  • Extractor (pymia.data.extraction.extractor module)
  • Indexing (pymia.data.extraction.indexing module)
  • Reader (pymia.data.extraction.reader module)
  • Selection (pymia.data.extraction.selection module)

Assembler (pymia.data.assembler module)

class pymia.data.assembler.ApplyTransformInteractionFn(transform: pymia.data.transformation.Transform)

Bases: pymia.data.assembler.AssembleInteractionFn

class pymia.data.assembler.AssembleInteractionFn

Bases: object

Function interface enabling interaction with the index_expression and the data before it gets added to the assembled prediction in SubjectAssembler.

__call__(key, data, index_expr, **kwargs)

Parameters

• key (str) – The identifier or key of the data.

• data (numpy.ndarray) – The data.

• index_expr (.IndexExpression) – The current index_expression that might be modified.

• **kwargs (dict) – Any other arguments

Returns

Modified data and modified index_expression

Return type

tuple

class pymia.data.assembler.Assembler

Bases: abc.ABC

Interface for assembling images from batch, which contain parts (chunks) of the images only.

abstract add_batch(to_assemble, sample_indices, last_batch=False, **kwargs)

Add the batch results to be assembled.

Parameters

• to_assemble (object, dict) – object or dictionary of objects to be assembled to an image.

• sample_indices (iterable) – iterable of all the sample indices in the processed batch

• last_batch (bool) – Whether the current batch is the last.

abstract get_assembled_subject(subject_index: int)

Parameters

subject_index (int) – Index of the assembled subject to be retrieved.

Returns

The assembled data of the subject (might be multiple arrays).

Return type

object

abstract property subjects_ready

The indices of the subjects that are finished assembling.

Type

list, set

class pymia.data.assembler.PlaneSubjectAssembler(datasource: pymia.data.extraction.datasource.PymiaDatasource, merge_fn=<function mean_merge_fn>, zero_fn=<function numpy_zeros>)

Bases: pymia.data.assembler.Assembler

Assembles predictions of one or multiple subjects where predictions are made in all three planes.

This class assembles the prediction from all planes (axial, coronal, sagittal) and merges the prediction according to merge_fn.

Assumes that the network output, i.e. to_assemble, is of shape (B, …, C) where B is the batch size and C is the numbers of channels (must be at least 1) and … refers to an arbitrary image dimension.

Parameters

• datasource (.PymiaDatasource) – The datasource

• merge_fn – A function that processes a sample. Args: planes: list with the assembled prediction for all planes. Returns: Merged numpy.ndarray

• zero_fn – A function that initializes the numpy array to hold the predictions. Args: shape: tuple with the shape of the subject’s labels, id: str identifying the subject. Returns: A np.ndarray

add_batch(to_assemble: Union[numpy.ndarray, Dict[str, numpy.ndarray]], sample_indices: numpy.ndarray, last_batch=False, **kwargs)

see Assembler.add_batch()

get_assembled_subject(subject_index: int)

see Assembler.get_assembled_subject()

property subjects_ready

see Assembler.subjects_ready()

class pymia.data.assembler.Subject2dAssembler(datasource: pymia.data.extraction.datasource.PymiaDatasource)

Bases: pymia.data.assembler.Assembler

Assembles predictions of two-dimensional images.

Two-dimensional images do not specifically require assembling. For pipeline compatibility reasons this class provides , nevertheless, a implementation for the two-dimensional case.

Parameters

datasource (.PymiaDatasource) – The datasource

add_batch(to_assemble: Union[numpy.ndarray, Dict[str, numpy.ndarray]], sample_indices: numpy.ndarray, last_batch=False, **kwargs)

see Assembler.add_batch()

get_assembled_subject(subject_index)

see Assembler.get_assembled_subject()

property subjects_ready

see Assembler.subjects_ready()

class pymia.data.assembler.SubjectAssembler(datasource: pymia.data.extraction.datasource.PymiaDatasource, zero_fn=<function numpy_zeros>, assemble_interaction_fn=None)

Bases: pymia.data.assembler.Assembler

Assembles predictions of one or multiple subjects.

Assumes that the network output, i.e. to_assemble, is of shape (B, …, C) where B is the batch size and C is the numbers of channels (must be at least 1) and … refers to an arbitrary image dimension.

Parameters

• datasource (.PymiaDatasource) – The datasource.

• zero_fn – A function that initializes the numpy array to hold the predictions. Args: shape: tuple with the shape of the subject’s labels. Returns: A np.ndarray

• assemble_interaction_fn (callable, optional) – A callable that may modify the sample and indexing before adding the data to the assembled array. This enables handling special cases. Must follow the .AssembleInteractionFn.__call__ interface. By default neither data nor indexing is modified.

add_batch(to_assemble: Union[numpy.ndarray, Dict[str, numpy.ndarray]], sample_indices: numpy.ndarray, last_batch=False, **kwargs)

Add the batch results to be assembled.

Parameters

• to_assemble (object, dict) – object or dictionary of objects to be assembled to an image.

• sample_indices (iterable) – iterable of all the sample indices in the processed batch

• last_batch (bool) – Whether the current batch is the last.

get_assembled_subject(subject_index: int)

see Assembler.get_assembled_subject()

property subjects_ready

see Assembler.subjects_ready()

Augmentation (pymia.data.augmentation module)

This module holds classes for data augmentation.

The data augmentation bases on the transformation concept (see pymia.data.transformation.Transform) and can easily be incorporated into the data loading process.

See also

The pymia documentation features an code example for Augmentation, which shows how to apply data augmentation in conjunction with the pymia.data package. Besides transformations from the pymia.data.augmentation module, transformations from the Python packages batchgenerators and TorchIO are integrated.

Warning

The augmentation relies on the random number generator of numpy. If you want to obtain reproducible result, set numpy’s seed prior to executing any augmentation:

>>> import numpy as np
>>> your_seed = 0
>>> np.random.seed(your_seed)

class pymia.data.augmentation.RandomCrop(shape: Union[int, tuple], axis: Optional[Union[int, tuple]] = None, p: float = 1.0, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Randomly crops the sample to the specified shape.

The sample shape must be bigger than the crop shape.

Notes

A probability lower than 1.0 might make not much sense because it results in inconsistent output dimensions.

Parameters

• shape (int, tuple) –

  The shape of the sample after the cropping. If axis is not defined, the cropping will be applied from the first dimension onwards of the sample. Use None to exclude an axis or define axis to specify the axis/axes to crop. E.g.:

  • shape=256 with the default axis parameter results in a shape of 256 x …

  • shape=(256, 128) with the default axis parameter results in a shape of 256 x 128 x …

  • shape=(None, 256) with the default axis parameter results in a shape of <as before> x 256 x …

  • shape=(256, 128) with axis=(1, 0) results in a shape of 128 x 256 x …

  • shape=(None, 128, 256) with axis=(1, 2, 0) results in a shape of 256 x <as before> x 256 x …

• axis (int, tuple) – Axis or axes to which the shape int or tuple correspond(s) to. If defined, must have the same length as shape.

• p (float) – The probability of the cropping to be applied.

• entries (tuple) – The sample’s entries to apply the cropping to.

class pymia.data.augmentation.RandomElasticDeformation(num_control_points: int = 4, deformation_sigma: float = 5.0, interpolators: tuple = (3, 1), spatial_rank: int = 2, fill_value: float = 0.0, p: float = 0.5, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Randomly transforms the sample elastically.

Notes

The code bases on NiftyNet’s RandomElasticDeformationLayer class (version 0.3.0).

Warning

Always inspect the results of this transform on some samples (especially for 3-D data).

Parameters

• num_control_points (int) – The number of control points for the b-spline mesh.

• deformation_sigma (float) – The maximum deformation along the deformation mesh.

• interpolators (tuple) – The SimpleITK interpolators to use for each entry in entries.

• spatial_rank (int) – The spatial rank (dimension) of the sample.

• fill_value (float) – The fill value for the resampling.

• p (float) – The probability of the elastic transformation to be applied.

• entries (tuple) – The sample’s entries to apply the elastic transformation to.

class pymia.data.augmentation.RandomMirror(axis: int = - 2, p: float = 1.0, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Randomly mirrors the sample along a given axis.

Parameters

• p (float) – The probability of the mirroring to be applied.

• axis (int) – The axis to apply the mirroring.

• entries (tuple) – The sample’s entries to apply the mirroring to.

class pymia.data.augmentation.RandomRotation90(axes: Tuple[int] = (- 3, - 2), p: float = 1.0, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Randomly rotates the sample 90, 180, or 270 degrees in the plane specified by axes.

Raises

UserWarning – If the plane to rotate is not rectangular.

Parameters

• axes (tuple) – The sample is rotated in the plane defined by the axes. Axes must be of length two and different.

• p (float) – The probability of the rotation to be applied.

• entries (tuple) – The sample’s entries to apply the rotation to.

class pymia.data.augmentation.RandomShift(shift: Union[int, tuple], axis: Optional[Union[int, tuple]] = None, p: float = 1.0, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Randomly shifts the sample along axes by a value from the interval [-p * size(axis), +p * size(axis)], where p is the percentage of shifting and size(axis) is the size along an axis.

Parameters

• shift (int, tuple) –

  The percentage of shifting of the axis’ size. If axis is not defined, the shifting will be applied from the first dimension onwards of the sample. Use None to exclude an axis or define axis to specify the axis/axes to crop. E.g.:

  • shift=0.2 with the default axis parameter shifts the sample along the 1st axis.

  • shift=(0.2, 0.1) with the default axis parameter shifts the sample along the 1st and 2nd axes.

  • shift=(None, 0.2) with the default axis parameter shifts the sample along the 2st axis.

  • shift=(0.2, 0.1) with axis=(1, 0) shifts the sample along the 1st and 2nd axes.

  • shift=(None, 0.1, 0.2) with axis=(1, 2, 0) shifts the sample along the 1st and 3rd axes.

• axis (int, tuple) – Axis or axes to which the shift int or tuple correspond(s) to. If defined, must have the same length as shape.

• p (float) – The probability of the shift to be applied.

• entries (tuple) – The sample’s entries to apply the shifting to.

Conversion (pymia.data.conversion module)

This module holds classes related to image conversion.

The main purpose of this module is the conversion between SimpleITK images and numpy arrays.

class pymia.data.conversion.NumpySimpleITKImageBridge

Bases: object

A numpy to SimpleITK bridge, which provides static methods to convert between numpy array and SimpleITK image.

static convert(array: numpy.ndarray, properties: pymia.data.conversion.ImageProperties) → SimpleITK.SimpleITK.Image

Converts a numpy array to a SimpleITK image.

Parameters

• array (np.ndarray) –

  The image as numpy array. The shape can be either:

  • shape=(n,), where n = total number of voxels

  • shape=(n,v), where n = total number of voxels and v = number of components per pixel (vector image)

  • shape=(<reversed image size>), what you get from sitk.GetArrayFromImage()

  • shape=(<reversed image size>,v), what you get from sitk.GetArrayFromImage()

    and v = number of components per pixel (vector image)

• properties (ImageProperties) – The image properties.

Returns

The SimpleITK image.

Return type

sitk.Image

class pymia.data.conversion.SimpleITKNumpyImageBridge

Bases: object

A SimpleITK to numpy bridge.

Converts SimpleITK images to numpy arrays. Use the NumpySimpleITKImageBridge to convert back.

static convert(image: SimpleITK.SimpleITK.Image) → Tuple[numpy.ndarray, pymia.data.conversion.ImageProperties]

Converts an image to a numpy array and an ImageProperties class.

Parameters

image (SimpleITK.Image) – The image.

Returns

The image as numpy array and the image properties.

Return type

A Tuple[np.ndarray, ImageProperties]

Raises

ValueError – If image is None.

Definition (pymia.data.definition module)

This module contains global definitions for the pymia.data package.

pymia.data.definition.KEY_CATEGORIES = 'categories'

pymia.data.definition.KEY_FILE_ROOT = 'file_root'

pymia.data.definition.KEY_IMAGES = 'images'

pymia.data.definition.KEY_INDEX_EXPR = 'index_expr'

pymia.data.definition.KEY_LABELS = 'labels'

pymia.data.definition.KEY_PLACEHOLDER_FILES = '{}_files'

pymia.data.definition.KEY_PLACEHOLDER_NAMES = '{}_names'

pymia.data.definition.KEY_PLACEHOLDER_NAMES_SELECTED = '{}_names_selected'

pymia.data.definition.KEY_PLACEHOLDER_PROPERTIES = '{}_properties'

pymia.data.definition.KEY_PROPERTIES = 'properties'

pymia.data.definition.KEY_SAMPLE_INDEX = 'sample_index'

pymia.data.definition.KEY_SHAPE = 'shape'

pymia.data.definition.KEY_SUBJECT = 'subject'

pymia.data.definition.KEY_SUBJECT_FILES = 'subject_files'

pymia.data.definition.KEY_SUBJECT_INDEX = 'subject_index'

Index expression (pymia.data.indexexpression module)

class pymia.data.indexexpression.IndexExpression(indexing: Optional[Union[int, tuple, List[int], List[tuple], List[list]]] = None, axis: Optional[Union[int, tuple]] = None)

Bases: object

Defines the indexing of a chunk of raw data in the dataset.

Parameters

• indexing (int, tuple, list) – The indexing. If int or list of int, individual entries of and axis are indexed. If tuple or list of tuple, the axis should be sliced.

• axis (int, tuple) – The axis/axes to the corresponding indexing. If tuple, the length has to be equal to the list length of indexing

expression

list of slice objects defining the slicing each axis

get_indexing()

Returns

a list tuples defining the indexing (i.e., None, index, (start, stop)) at each axis. Can be used to generate a new index expression.

Return type

list

set_indexing(indexing: Union[int, tuple, slice, List[int], List[tuple], List[list]], axis: Optional[Union[int, tuple]] = None)

Subject file (pymia.data.subjectfile module)

class pymia.data.subjectfile.FileCategory(entries=None)

Bases: object

class pymia.data.subjectfile.SubjectFile(subject: str, **file_groups)

Bases: object

Holds the file information of a subject.

Parameters

• subject (str) – The subject identifier.

• **file_groups (dict) – The groups of file types containing the file path entries.

get_all_files()

Returns

All file path entries of a subject flattened (without groups/category).

Return type

dict

Transformation (pymia.data.transformation module)

class pymia.data.transformation.ClipPercentile(upper_percentile: float, lower_percentile: Optional[float] = None, loop_axis=None, entries=('images',))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.ComposeTransform(transforms: Iterable[pymia.data.transformation.Transform])

Bases: pymia.data.transformation.Transform

class pymia.data.transformation.IntensityNormalization(loop_axis=None, entries=('images',))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.IntensityRescale(lower, upper, loop_axis=None, entries=('images',))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.LambdaTransform(lambda_fn, loop_axis=None, entries=('images',))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.LoopEntryTransform(loop_axis=None, entries=())

Bases: pymia.data.transformation.Transform, abc.ABC

static loop_entries(sample: dict, fn, entries, loop_axis=None)

abstract transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.Mask(mask_key: str, mask_value: int = 0, masking_value: float = 0.0, loop_axis=None, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

class pymia.data.transformation.Permute(permutation: tuple, entries=('images', 'labels'))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.RandomCrop(size: tuple, loop_axis=None, entries=('images', 'labels'))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.Relabel(label_changes: Dict[int, int], entries=('labels',))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.Reshape(shapes: dict)

Bases: pymia.data.transformation.LoopEntryTransform

Initializes a new instance of the Reshape class.

Parameters

shapes (dict) – A dict with keys being the entries and the values the new shapes of the entries. E.g. shapes = {defs.KEY_IMAGES: (-1, 4), defs.KEY_LABELS : (-1, 1)}

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.SizeCorrection(shape: Tuple[Union[None, int], ...], pad_value: int = 0, entries=('images', 'labels'))

Bases: pymia.data.transformation.Transform

Size correction transformation.

Corrects the size, i.e. shape, of an array to a given reference shape.

Initializes a new instance of the SizeCorrection class.

Parameters

• shape (tuple of ints) – The reference shape in NumPy format, i.e. z-, y-, x-order. To not correct an axis dimension, set the axis value to None.

• pad_value (int) – The value to set the padded values of the array.

• () (entries) –

class pymia.data.transformation.Squeeze(entries=('images', 'labels'), squeeze_axis=None)

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

class pymia.data.transformation.Transform

Bases: abc.ABC

class pymia.data.transformation.UnSqueeze(axis=- 1, entries=('images', 'labels'))

Bases: pymia.data.transformation.LoopEntryTransform

transform_entry(np_entry, entry, loop_i=None) → numpy.ndarray

pymia.data.transformation.check_and_return(obj, type_)