Version 2.0.0

Version 2.0.0

Version 2.0.0 is a major refactoring of the MIRP package.

Major changes

• MIRP was previously configured using two xml files: config_data.xml for configuring
  directories, data to be read, etc., and config_settings.xml for configuring experiments.
  While these two files can still be used, MIRP can now be configured directly, without using these files.

• The main functions of MIRP (mainFunctions.py) have all been re-implemented.

  • mainFunctions.extract_features is now extractFeaturesAndImages.extract_features (functional form) or
    extractFeaturesAndImages.extract_features_generator (generator). The replacements allow for both writing
    feature values to a directory and returning them as function output.
  • mainFunctions.extract_images_to_nifti is now extractFeaturesAndImages.extract_images (functional form) or
    extractFeaturesAndImages.extract_images_generator (generator). The replacements allow for both writing
    images to a directory (e.g., in NIfTI or numpy format) and returning them as function output.
  • mainFunctions.extract_images_for_deep_learning has been replaced by
    deepLearningPreprocessing.deep_learning_preprocessing (functional form) and
    deepLearningPreprocessing.deep_learning_preprocessing_generator (generator).
  • mainFunctions.get_file_structure_parameters and mainFunctions.parse_file_structure are deprecated, as the
    the file import system used in version 2 no longer requires a rigid directory structure.
  • mainFunctions.get_roi_labels is now extractMaskLabels.extract_mask_labels.
  • mainFunctions.get_image_acquisition_parameters is now extractImageParameters.extract_image_parameters.

• MIRP previously relied on ImageClass and RoiClass objects. These have been completely replaced by GenericImage
  (and its subclasses, e.g. CTImage) and BaseMask objects, respectively. New image modalities can be added as
  subclass of GenericImage in the mirp.images submodule.

• File import, e.g. from DICOM or NIfTI files, in was previously implemented in an ad-hoc manner, and required a rigid
  directory structure. Now, file import is implemented using an object-oriented approach, and directory structures
  are more flexible. File import of new modalities can be implemented as a relevant subclass of ImageFile.

• MIRP uses type hinting, and makes use of the Self type hint introduced in Python 3.11. MIRP therefore requires
  Python 3.11 or later.

Minor changes

• MIRP now uses the ray package for parallel processing.

Version 1.3.0 (dev - unreleased)

Minor changes

• SimpleITK has been removed as a dependency. Handling of non-DICOM imaging is now done through itk itself.
• Rotation - as a perturbation or augmentation operation - is now performed as part of the interpolation process.
  Previously, rotation was implemented using scipy.ndimage.rotate. This, combined with any translation or
  interpolation operation would involve two interpolation steps. Aside from removing a computationally intensive
  step, this also prevents unnecessary image degradation through the interpolation process. The new implementation
  operates using affine matrix transformations.
• Discretisation of intensities after filtering (i.e. intensities of response maps) now uses a fixed bin number
  method with 16 bins by default. Previously, no default was set, which could lead to unintended results. These
  parameters can be manually specified using the response_map_discretisation_method,
  response_map_discretisation_bin_width, and response_map_discretisation_n_bins arguments; or alternatively
  using the discretisation_method, discretisation_bin_width and discretisation_n_bins parameters of the
  img_transform section of the settings configuration file.

Fixes

• Fixed a deprecation warning caused by slic of the scikit-image module.
• Fixed incorrect merging of contours of the same region of interest (ROI) in the same slice. Previously, each contour
  was converted to a mask individually, and merged with the segmentation mask using OR operations. This functions
  perfectly for contours that represent separate objects spatially. However, holes in RTSTRUCT objects are not
  always represented by a single contour. They can also be represented by a separate contour (of the same region of
  interest) that is contained within a larger contour. For those RTSTRUCT objects, holes would disappear. This has
  now been fixed by first collecting all contours of a ROI for each slice, prior to converted them to a segmentation
  mask.