Postprocess module

PyMIALSRTK postprocessing functions.

It encompasses a High Resolution mask refinement and an N4 global bias field correction.

BinarizeImage

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Runs the MIAL SRTK mask image module. .. rubric:: Example

>>> from pymialsrtk.interfaces.postprocess import BinarizeImage
>>> maskImg = MialsrtkMaskImage()
>>> maskImg.inputs.input_image = 'input_image.nii.gz'
Mandatory Inputs

input_image (a pathlike object or string representing a file) – Input image filename to be binarized.

Outputs

output_srmask (a pathlike object or string representing a file) – Image mask (binarized input).

FilenamesGeneration

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Generates final filenames from outputs of super-resolution reconstruction.

Example

>>> from pymialsrtk.interfaces.postprocess import FilenamesGeneration
>>> filenamesGen = FilenamesGeneration()
>>> filenamesGen.inputs.sub_ses = 'sub-01'
>>> filenamesGen.inputs.stacks_order = [3,1,4]
>>> filenamesGen.inputs.sr_id = 3
>>> filenamesGen.inputs.use_manual_masks = False
>>> filenamesGen.run()
Mandatory Inputs

  • sr_id (an integer) – Super-Resolution id.

  • stacks_order (a list of items which are any value) – List of stack run-id that specify the order of the stacks.

  • sub_ses (a string) – Subject and session BIDS identifier to construct output filename.

  • use_manual_masks (a boolean) – Whether masks were computed or manually performed.

Outputs

substitutions (a list of items which are any value) – Output correspondance between old and new filenames.

FilenamesGeneration.m_substitutions = []

MialsrtkN4BiasFieldCorrection

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Runs the MIAL SRTK slice by slice N4 bias field correction module.

This tools implements the method proposed by Tustison et al. [1]_ slice by slice.

References

1

Tustison et al.; Medical Imaging, IEEE Transactions, 2010. (link to paper)

Example

>>> from pymialsrtk.interfaces.preprocess import MialsrtkSliceBySliceN4BiasFieldCorrection
>>> N4biasFieldCorr = MialsrtkSliceBySliceN4BiasFieldCorrection()
>>> N4biasFieldCorr.inputs.bids_dir = '/my_directory'
>>> N4biasFieldCorr.inputs.input_image = 'sub-01_acq-haste_run-1_SR.nii.gz'
>>> N4biasFieldCorr.inputs.input_mask = 'sub-01_acq-haste_run-1_mask.nii.gz'
>>> N4biasFieldCorr.run()
Mandatory Inputs

  • bids_dir (a string or os.PathLike object referring to an existing directory) – BIDS root directory.

  • input_image (a pathlike object or string representing a file) – Input image filename to be normalized.

Optional Inputs

  • input_mask (a pathlike object or string representing a file) – Input mask filename.

  • out_fld_postfix (a string) – (Nipype default value: _gbcorrfield)

  • out_im_postfix (a string) – (Nipype default value: _gbcorr)

Outputs

  • output_field (a pathlike object or string representing a file) – Output bias field extracted from input image.

  • output_image (a pathlike object or string representing a file) – Output corrected image.

MialsrtkRefineHRMaskByIntersection

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Runs the MIALSRTK mask refinement module.

It uses the Simultaneous Truth And Performance Level Estimate (STAPLE) by Warfield et al. [1]_.

References

1

Warfield et al.; Medical Imaging, IEEE Transactions, 2004. (link to paper)

Example

>>> from pymialsrtk.interfaces.postprocess import MialsrtkRefineHRMaskByIntersection
>>> refMask = MialsrtkRefineHRMaskByIntersection()
>>> refMask.inputs.bids_dir = '/my_directory'
>>> refMask.inputs.input_images = ['sub-01_acq-haste_run-1_T2w.nii.gz','sub-01_acq-haste_run-2_T2w.nii.gz']
>>> refMask.inputs.input_masks = ['sub-01_acq-haste_run-1_mask.nii.gz','sub-01_acq-haste_run-2_mask.nii.gz']
>>> refMask.inputs.input_transforms = ['sub-01_acq-haste_run-1_transform.txt','sub-01_acq-haste_run-2_transform.nii.gz']
>>> refMask.inputs.input_sr = 'sr_image.nii.gz'
>>> refMask.run()
Mandatory Inputs

  • bids_dir (a string or os.PathLike object referring to an existing directory) – BIDS root directory.

  • input_sr (a pathlike object or string representing a file) – SR image filename.

Optional Inputs

  • in_use_staple (a boolean) – Use STAPLE for voting (default is True). If False, Majority voting is used instead. (Nipype default value: True)

  • input_images (a list of items which are a pathlike object or string representing a file) – Image filenames used in SR reconstruction.

  • input_masks (a list of items which are a pathlike object or string representing a file) – Mask filenames.

  • input_rad_dilatation (an integer) – Radius of the structuring element (ball). (Nipype default value: 1)

  • input_transforms (a list of items which are a pathlike object or string representing a file) – Transformation filenames.

  • out_lrmask_postfix (a string) – Suffix to be added to the Low resolution input_masks. (Nipype default value: _LRmask)

  • out_srmask_postfix (a string) – Suffix to be added to the SR reconstruction filename to construct output SR mask filename. (Nipype default value: _srMask)

Outputs

  • output_lrmasks (a list of items which are a pathlike object or string representing a file) – Output low-resolution reconstruction refined masks.

  • output_srmask (a pathlike object or string representing a file) – Output super-resolution reconstruction refined mask.