The BLENDS Method for Data Augmentation of 4-Dimensional Brain Images

Manuscript currently under review.

Brain Library Enrichment through Nonlinear Deformation Synthesis (BLENDS) is a method for data augmentation of brain images, including 3D+time images such as fMRI.

Dependencies

Python packages

See the requirements.txt file.

• numpy >= 1.15.4
• pandas >= 0.25.3
• nipype >= 1.4.2
• nibabel >= 2.4.0.dev0
• tqdm >= 4.49.0
• SimpleITK >= 1.1.0
• nilearn >= 0.6.2
• scikit-image >= 0.16.2
• matplotlib >= 2.2.2
• transforms3d >= 0.3.1
• bids >= 0.0

Neuroimage processing tools

These tools should be installed and added to the system PATH.

• ANTs >= 2.2.0
• ROBEX >= 1.2
• FSL >= 5.0.10

Usage

Input images are assumed to be organized in BIDS-compliant directory and filename structure. Tools like PyBIDS can be used to organize existing neuroimaging datasets into BIDS structure.

The blends.blending.WarpBlender class contains the main functionality for running BLENDS on an fMRI or a pair of fMRI + T1-weighted MRI (sMRI) from the same subject. Running BLENDS involves initializing WarpBlender with a directory containing precomputed nonlinear warps and then calling 3 methods. Steps:

0. Create a WarpBlender using a directory of precomputed MNI->subject warps (see below).

augmenter = WarpBlender(warp_directory)

1. Use prepare_source() to skullstrip the input image and register it to the MNI template.

augmenter.prepare_source(func_path, registration='func')

2. Use generate_warp() to compute the new spatially blended warp. By default, this will blend together 4 of the original warps.

augmenter.generate_warp()

3. Call augment().

augmenter.augment(output_path)

See the docstring for WarpBlender for more information on method arguments.

Generating warp pool

An example script for generating a pool of MNI->subject warps is included: examples/generate_warp_pool.py

Other functionality

Also included are a class for performing random affine augmentations (blends.affine.AffineAugmenter) and an implementation of the EROS SMOTE method (blends.smote.SmoteEros).

Acknowledgements

The BOLD MNI152 template in data/ was obtained from the templateflow repository.

License

Copyright (c) 2022 The University of Texas Southwestern Medical Center. All rights reserved.

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