dival.datasets.standard module

Provides standard datasets for benchmarking.

dival.datasets.standard.get_standard_dataset(name, **kwargs)

Return a standard dataset by name.

The standard datasets are (currently):

'ellipses'

A typical synthetical CT dataset with ellipse phantoms.

EllipsesDataset is used as ground truth dataset, a ray transform with parallel beam geometry using 30 angles is applied, and white gaussian noise with a standard deviation of 2.5% (i.e. 0.025 * mean(abs(observation))) is added.

In order to avoid the inverse crime, the ground truth images of shape (128, 128) are upscaled by bilinear interpolation to a resolution of (400, 400) before the ray transform is applied (whose discretization is different from the one of ray_trafo).

Attributes of the returned dataset:

ray_trafoodl.tomo.RayTransform

Ray transform corresponding to the noiseless forward operator.

get_ray_trafo(**kwargs)function

Function that returns a ray transform corresponding to the noiseless forward operator. Keyword arguments (e.g. impl) are forwarded to the RayTransform constructor.

'lodopab'

The LoDoPaB-CT dataset, which is documented in the Data Descriptor article https://www.nature.com/articles/s41597-021-00893-z and hosted on https://zenodo.org/record/3384092. It is a simulated low dose CT dataset based on real reconstructions from the LIDC-IDRI dataset.

The dataset contains 42895 pairs of images and projection data. For simulation, a ray transform with parallel beam geometry using 1000 angles and 513 detector pixels is used. Poisson noise corresponding to 4096 incident photons per pixel before attenuation is applied to the projection data.

Attributes of the returned dataset:

ray_trafoodl.tomo.RayTransform

Ray transform corresponding to the noiseless forward operator.

Methods of the returned dataset:

get_ray_trafo(**kwargs)

Function that returns a ray transform corresponding to the noiseless forward operator. Keyword arguments (e.g. impl) are forwarded to the RayTransform constructor.

Parameters

• name (str) – Name of the dataset.

• kwargs (dict) –

  Keyword arguments. Supported parameters for the datasets are:

  'ellipses'

  impl{'skimage', 'astra_cpu', 'astra_cuda'}, optional

  Implementation passed to odl.tomo.RayTransform Default: 'astra_cuda'.

  fixed_seedsdict or bool, optional

  Seeds to use for random ellipse generation, passed to EllipsesDataset.__init__(). Default: False.

  fixed_noise_seedsdict or bool, optional

  Seeds to use for noise generation, passed as noise_seeds to GroundTruthDataset.create_pair_dataset(). If True is passed (the default), the seeds {'train': 1, 'validation': 2, 'test': 3} are used.

  'lodopab'

  num_anglesint, optional

  Number of angles to use from the full 1000 angles. Must be a divisor of 1000.

  observation_model{'post-log', 'pre-log'}, optional

  The observation model to use. Default is 'post-log'.

  min_photon_countfloat, optional

  Replacement value for a simulated photon count of zero. If observation_model == 'post-log', a value greater than zero is required in order to avoid undefined values. The default is 0.1, both for 'post-log' and 'pre-log' model.

  sorted_by_patientbool, optional

  Whether to sort the samples by patient id. Useful to resplit the dataset. Default: False.

  impl{'skimage', 'astra_cpu', 'astra_cuda'}, optional

  Implementation passed to odl.tomo.RayTransform Default: 'astra_cuda'.

Returns

dataset – The standard dataset. It has an attribute standard_dataset_name that stores its name.

Return type

Dataset