/LogEuclidean-Intro

Resources and example on the Log-Euclidean framework for DTI processing

Primary LanguageJupyter NotebookMIT LicenseMIT

Log-Euclidean and DTI Introduction

This repository contains resources that helps in the understanding of Diffusion Tensor Imaging (DTI) processing using the Log-Euclidean framework. It is recommended to read the proposed articles in order to fully appreciate the Jupyter Notebook.

Articles

Diffusion Tensor Imaging Basics

  • Mori, S., & Zhang, J. (2006). Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research.
  • Maier-Hein, K. H., Neher, P. F., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., … Descoteaux, M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications

Log-Euclidean Framework

  • Arsigny, V., Fillard, P., Pennec, X., & Ayache, N. (2006). Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine
  • Arsigny, V., Fillard, P., Pennec, X., & Ayache, N. (2006). Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM Journal on Matrix Analysis and Applications

Generative Adversarial Networks (GANs)

  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems. Neural information processing systems foundation.
  • Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN.
  • Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision
  • Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis

Deep Neural Networks for SPD Matrices Processing and DTI Synthesis

  • Huang, Z., & Van Gool, L. (2017). A riemannian network for SPD matrix learning. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017
  • Huang, Z., Wu, J., & Van Gool, L. (2019). Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets. Proceedings of the AAAI Conference on Artificial Intelligence
  • Gu, X., Knutsson, H., Nilsson, M., & Eklund, A. (2019). Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks. Lecture Notes in Computer Science
  • Zhong, J., Wang, Y., Li, J., Xue, X., Liu, S., Wang, M., … Li, X. (2020). Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: Application to neonatal white matter development. BioMedical Engineering Online

Fiber Bundle Segmentation

  • Wasserthal, J., Neher, P. F., & Maier-Hein, K. H. (2018). Fast and accurate white matter bundle segmentation.

Advanced Topics

  • Ionescu, C., Vantzos, O., & Sminchisescu, C. (2015). Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE International Conference on Computer Vision
  • Brooks, D., Schwander, O., Barbaresco, F., Schneider, J.-Y., & Cord, M. (2019). Riemannian batch normalization for SPD neural networks.

Tools

Visualization

Deep Learning

Log-Euclidean Introduction Notebook

  1. Clone the project
  2. Setup a virtual environment
  3. Install the dependencies:
    • Install Pytorch from https://pytorch.org/
    • Install other libraries: pip install -r requirements.txt
  4. Install a Jupyter kernel
  5. Run Jupyter and select the provided Jupyter notebook
    • Run: jupyter notebook
    • Select LogEuclideanIntro.ipynb and make sure that the selected kernel is : .venv