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NDFlow

This is the code repository for Nonparametric Density Flows for MRI Intensity Normalisation, presented at the MICCAI 2018 conference. We have also made available an extended manuscript on arXiv, including some mathematical proofs and additional experimental results.

If you would like to use this tool, please consider citing our work according to the following BibTeX entry:

@inproceedings{castro2018ndflow,
    title = {Nonparametric Density Flows for {MRI} Intensity Normalisation},
    author = {Castro, Daniel C. and Glocker, Ben},
    year = {2018}
    booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2018},
    pages = {206--214},
    series = {LNCS},
    volume = {11070},
    publisher = {Springer, Cham},
    address = {Granada, Spain},
    doi = {10.1007/978-3-030-00928-1_24},
    eprint = {arXiv:1806.02613},
}

Dependencies

  • Python 3.6 (not tested with earlier versions)
  • numpy
  • scipy
  • SimpleITK (only for the scripts)
  • matplotlib (only for the scripts and if you use the --interactive option)

Installation

Simply clone the Git repository, cd into the directory and run pip install ..

API Usage

The most important functionality is made available in the ndflow namespace, which includes the following functions (see corresponding docstrings in ndflow/api.py for details):

  • ndflow.estimate: GMM fitting
  • ndflow.align: affine alignment between GMMs
  • ndflow.match: minimise L2 divergence between GMMs
  • ndflow.warp: simulate diffeomorphic flow between two GMM densities

These functions should be used if dealing directly with numpy arrays and ndflow.models.mixture.MixtureModel instances.

CLI Usage

We mirror the API with scripts that also handle I/O (with SimpleITK) and workflow-specific tasks like GMM averaging, available in the ndflow.tools package:

  • estimate.py: fit GMMs to one or a collection of image files
  • average.py: compute an 'average' GMM out of a collection of GMMs
  • match.py: match one or a collection of GMMs to a target GMM
  • warp.py: transform intensities in one or a collection of images according to the respective matched GMMs

These scripts wrap functions which may also be used programmatically.

If ndflow is installed with pip, these scripts are directly accessible as terminal commands: ndflow-estimate, ndflow-average, ndflow-match, and ndflow-warp, respectively. These are the usage instructions as obtained with the -h option:

usage: ndflow-estimate [-h] [-b BACKGROUND] [-l LEVELS] input output

NDFlow - density estimation

positional arguments:
  input                 input image file or directory. If a directory is
                        given, will process all image files inside it.
  output                output directory to store density estimation results
                        (*.pickle).

optional arguments:
  -h, --help            show this help message and exit
  -b BACKGROUND, --background BACKGROUND
                        threshold for background intensities. If given, voxels
                        with intensity <= background will be excluded,
                        otherwise the entire image will be used.
  -l LEVELS, --levels LEVELS
                        number of levels at which to quantise the intensities.
                        If omitted, data will be cast to integer.

usage: ndflow-average [-h] [-i] input output

NDFlow - GMM averaging

positional arguments:
  input              input GMMs directory
  output             output GMM file

optional arguments:
  -h, --help         show this help message and exit
  -i, --interactive  plot the results

usage: ndflow-match [-h] [-o OUTPUT] source target

NDFlow - density matching

positional arguments:
  source                source GMM file or directory. If a directory is given,
                        will process all GMM files inside it.
  target                target GMM file

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        output directory. Defaults to source directory.

usage: ndflow-warp [-h] [-m MATCH] input output

NDFlow - image intensity warping

positional arguments:
  input                 input image file or directory. If a directory is
                        given, will process all image files inside it.
  output                output image directory. Should be different from input
                        directory, as normalised image(s) will be saved with
                        the same filename.

optional arguments:
  -h, --help            show this help message and exit
  -m MATCH, --match MATCH
                        matches directory. Defaults to input image directory.

Group Normalisation

  • imgs_dir: Input images directory
  • gmms_dir: Directory for the estimated Gaussian mixture models (GMMs)
  • average_gmm_file: File path for the average GMM (i.e. 'intensity atlas')
  • matches_dir: Directory for density matching results
  • out_imgs_dir: Normalised images directory (different from imgs_dir, as images will be saved with the same filename)
> ndflow-estimate <imgs_dir> <gmms_dir>
> ndflow-average <gmms_dir> <average_gmm_file>
> ndflow-match <gmms_dir> <average_gmm_file> -m <matches_dir>
> ndflow-warp <imgs_dir> <matches_dir> -o <out_imgs_dir>

Image-to-Image Normalisation

  • src_img_file: Source image file
  • tgt_img_file: Target image file
  • src_gmm_file: Source GMM file (in source image directory with src_img_file prefix)
  • tgt_gmm_file: Target GMM file (in target image directory with tgt_img_file prefix)
  • out_img_dir: Normalised image directory (different from imgs_dir, as image will be saved with the same filename)
> ndflow-estimate <src_img_file>
> ndflow-estimate <tgt_img_file>
> ndflow-match <src_gmm_file> <tgt_gmm_file>
> ndflow-warp <src_img_file> <out_img_dir>