MR acquisition-invariant neural network
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MR Acquisition-Invariant Neural Network

This repository contains experiments and code accompanying the paper:

"MR Acquisition-Invariant Feature Extraction using Siamese Neural Networks"

which is currently under review. The preprint is on ArXiv.

MRAI-net takes patches from MRI-scans of two different scanners and maps them to feature vectors such that pairs of patches from the same tissue but from different scanners become similar, while pairs of patches from different tissues - regardless of scanner - remain dissimilar. It performs this mapping by propagating a patch through a convolutional neural network and comparing distances between pairs of patches in the final representation layer.

The patches, represented in this final layer, are visualized in the figure below. Colors (blue, green, yellow) represent tissues, and markers (crosses, squares) represent scanners. The lines in the left figure indicate whether a pair of patches should be pulled closer together (green) or pushed further apart (purple). At the start of training (left), patches are somewhat randomly distributed throughout the space. When training is complete, patches from the same tissue lie close together, regardless of scanner. Note that a patch, represented in this final layer, has become a two-dimensional feature vector.

MRAI-net representation of patches before training MRAI-net representation of patches after training


mrainet requires Python version>=3.5. Installation can be done through:

pip install mrainet

PyPI takes care of all dependencies. However, to ensure that these don't mess up your current python environment, you should set up a virtual one. If you're familiar with conda, you can do this through:

conda env create -f environment.yml
source activate mrainet


Please visit the examples section in the documentation to get started.

To give you an impression of how the module is used, here's an example call:

from mrainet.mraicnn import MRAIConvolutionalNeuralNetwork

M = MRAIConvolutionalNeuralNetwork()

H = M.feedforward(X)

where X are MRI-scans from the source scanner, Y are their corresponding segmentations, Z are MRI-scans from the target scanner, and U is its corresponding incomplete segmentations (nearly all are NaN). H represents the patches fed through the network and embedded in mrainet's penultimate layer.

For more information on individual classes, methods and functions, visit the docs.


In order to isolate MRI-scanner acquisition-based variation in medical images, we simulated datasets of varying scan protocol parameters using SIMRI. High-quality human brain phantoms were used, which can be found here:

The original code for SIMRI can be downloaded from here. Our repo contains a modified version of SIMRI that includes the additional NMR relaxation times, as described in the paper's appendix. For more information on how to run your own simulations, see the example shell script named in the folder data/brainweb/simulator.

Real data consisted of the MRBrainS data set, known from the brain tissue segmentation challenge at MICCAI 2013.

We used only the 5 training scans provided, as the labels for the remaining 15 scans have not been released.


Experimental scripts from the paper are included in this repository:

  • exp-mrai-cnn_b1b3: simulated data from MRI-scanners with different acquisition protocols.
  • exp_mrai-cnn_b1mb: simulated data as souce and real data as target, for different field strengths.
  • exp_mrai-cnn_b3mb: simulated data as souce and real data as target, for equivalent field strengths.

For more information on experiment parameters and setup, see the README in the experiments folder.


Bugs, comments and questions can be submitted to the issues tracker.