MIAL Super-Resolution Toolkit v2.0.0

Version 2.0.0

Date: November 25, 2020

This corresponds to the first release of the second version of the MIAL
Super-Resolution Toolkit, which has evolved massively over the last
years in terms of the underlying codebase and the scope of the
functionality provided, following recent advances in standardization of
neuroimaging data organization and processing workflows.

Major changes

• Adoption of the Brain Imaging Data Structure
  standard for data organization and
  the sample dataset available in data/ has been modified accordingly.
  (See BIDS and BIDS App standards <cmpbids> for more details)
• MIALSRTK is going to Python with the creation of the pymialsrtk
  workflow library which extends the Nipype dataflow
  library with the
  implementation of interfaces to all C++ MIALSRTK tools connected in
  a common workflow to perform super-resolution reconstruction of
  fetal brain MRI with data provenance and execution detail
  recordings. (See API Documentation <api-doc>)
• Docker image encapsulating MIALSRTK is distributed as a BIDS App, a
  standard for containerized workflow that handles BIDS datasets with
  a set of predefined commandline input argument. (See
  BIDS App Commadline Usage <cmdusage> for more details)
• Main documentation of MIALSRTK is rendered using readthedocs at
  https://mialsrtk.readthedocs.io/.

New feature

• pymialsrtk implements an automatic brain extraction (masking)
  module based on a 2D U-Net (Ronneberger et al. [Ref1]_) using the
  pre-trained weights from Salehi et al. [Ref2]_ (See pull request
  4).
  It is integrated in the BIDS App workflow by default.

• pymialsrtk implements a module for automatic stack reference
  selection and ordering (masking) based on the tracking of the brain
  mask centroid slice by slice (See pull request
  34)

* pymialsrtk implements for convenience a Python wrapper that
generates the Docker command line of the BIDS App for you, prints it out
for reporting purposes, and then executes it without further action
needed (See pull request
47)

Software development life cycle

• Adopt CircleCI for continuous integration testing and run the
  following regression tests:

  • Test 01: Run BIDS App on the sample data/ BIDS dataset with
    the --manual_masks option.
  • Test 02: Run BIDS App on the sample data/ BIDS dataset with
    automated brain extraction (masking).

  See CircleCI project
  page.

• Use Codacy to support code reviews and
  monitor code quality over time.

• Use coveragepy
  in CircleCI during regression tests of the BIDS app and create code
  coverage reports published on our Codacy project
  page.

More...

Please check pull request 2, pull request 4, pull request 34, pull request 39, pull request 47
for more change details and development discussions.