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A deep learning framework for superficial white matter parcellation, code release via SlicerDMRI

Key Investigators

Project Description

We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA) (Xue et al 2022, ISBI), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. We perform evaluation on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions. In this project week, we work on releasing the code of SupWMA. We provide the trained model and testing samples for demonstration.


  1. Code cleaning
  2. Release code and pre-trained model
  3. Documentation and testing samples

Approach and Plan

  1. Release code and pre-trained model at: https://github.com/SlicerDMRI/SupWMA
  2. Provide the instruction of SupWMA framework usage
  3. Upload the testing sample and demonstration script.

Progress and Next Steps

  1. Code and pre-trained model were released
  2. Instruction, testing sample and demonstration script were provided
  3. Update the training details to help user train SupWMA on custom data.
  4. Intergate SupWMA into SlicerDMRI so that users can use it via Slicer interface.





Background and References

Tengfei Xue, Fan Zhang, Chaoyi Zhang, Yuqian Chen, Yang Song, Nikos Makris, Yogesh Rathi, Weidong Cai, Lauren J. O’Donnell. “SupWMA: consistent and efficient tractography parcellation of superficial white matter with deep learning.” ISBI (2022).