# SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning

arXiv: https://arxiv.org/abs/2201.12528 · HTML: https://mlsanity.com/paper/2201.12528/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [Tractogram filtering of anatomically non-plausible fibers with geometric deep learning](https://mlsanity.com/paper/2003.11013/) (2020)
- [Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation](https://mlsanity.com/paper/2107.04938/) (2021)
- [DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks](https://mlsanity.com/paper/1906.03051/) (2019)

## Built on by (most cited first, 13 of 13)

- [Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions](https://mlsanity.com/paper/2207.08975/) (2022)
- [Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation](https://mlsanity.com/paper/2205.00627/) (2022)
- [TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance](https://mlsanity.com/paper/2307.03982/) (2023)
- [TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation](https://mlsanity.com/paper/2307.09000/) (2023)
- [White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning](https://mlsanity.com/paper/2207.02402/) (2022)
- [TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography](https://mlsanity.com/paper/2210.07411/) (2022)
- [A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject](https://mlsanity.com/paper/2303.14371/) (2023)
- [White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging](https://mlsanity.com/paper/2407.19460/) (2024)
- [MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages](https://mlsanity.com/paper/2503.06598/) (2025)
- [DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography](https://mlsanity.com/paper/2211.08119/) (2022)
- [DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2510.24770/) (2025)
- [GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter](https://mlsanity.com/paper/2303.01147/) (2023)
- [A foundation for systematic analysis of transformers and RNNs for tractography](https://mlsanity.com/paper/2610.01894/) (2026)

## Abstract (arXiv)

White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity. We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is modified for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. 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. Compared to several state-of-the-art methods, SupWMA obtains a highly consistent and accurate SWM parcellation result. In addition, the computational speed of SupWMA is much faster than other methods.