Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit: MIAL Super-Resolution Toolkit v2.0.2

Medical Image Analysis Laboratory Super-Resolution ToolKit 2 (MIALSRTK2) consists of a set of C++ and Python processing and workflow tools necessary to perform motion-robust super-resolution fetal MRI reconstruction in the BIDS Apps framework.

This corresponds to the second release of MIAL Super-Resolution Toolkit 2.

What's changed

New feature

• pymialsrtk enables to fix the maximal amount of memory (in Gb) that could be used by the pipelines at execution with the --memory MEMORY_Gb option flag. (See pull request 92).

• pymialsrtk generates a HTML processing report for each subject in sub-\<label>/report/sub-\<label>.html. It includes the following:

  • Pipeline/workflow configuration summary
  • Nipype workflow execution graph
  • Link to the processing log
  • Plots for the quality check of the automatic reordering step based on the motion index.
  • Three orthogonal cuts of the reconstructed image
  • Computing environment summary

  (See pull requests 97, 102, and 103).

Major change

• The method pymialsrtk.postprocess.binarize_image() has been modified and encapsulated in a new interface called pymialsrtk.postprocess.BinarizeImage.

Python update

• From 3.6.8 to 3.7.10

New package

• pandas 1.1.5
• sphinxcontrib-apidoc 0.3.0 (required to build documentation)
• sphinxcontrib-napoleon 0.7 (required to build documentation)

Package update

• traits from 5.1.2 to 6.3.0
• nipype from 1.6.0 to 1.7.0
• nilearn from 0.7.1 to 0.8.1
• numpy from 1.16.6 to 1.21.3
• scikit-learn from 0.20 to 1.0.1
• scikit-image from 0.14 to 0.16.2

Bug fix

• Correct the filename of the high-resolution brain mask generated by the data sinker in mialsrtk-<variant>/sub-<label>/anat. (See pull request 92)

• mialsrtkImageReconstruction updates the reference image used for slice-to-volume registration using the high-resolution image reconstructed by SDI at the previous iteration.

• The following Sphinx extension packages were added to the conda environment, that were required if one wish to build the documentation locally:

  • sphinxcontrib-apidoc 0.3.0
  • sphinxcontrib-napoleon 0.7

Note

It was not possible to update the version of tensorflow for the moment. All versions of tensorflow greater than 1.14 are in fact compiled with a version of GCC much more recent than the one available in Ubuntu 14.04. This seems to cause unresponsiveness of the preprocess.BrainExtraction interface node which can get stuck while getting access to the CPU device.

Software development life cycle

• Use PEP 8 Speaks, a GitHub app to automatically review Python code style over Pull Requests. Configuration described by .pep8speaks.yml

More...

Please check main pull requests 70 and 110 for more change details and development discussions.

Full Changelog: https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/compare/v2.0.1...v2.0.2