# 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

arXiv: https://arxiv.org/abs/2207.08975 · HTML: https://mlsanity.com/paper/2207.08975/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 97 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [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)
- [Token-Level Supervised Contrastive Learning for Punctuation Restoration](https://mlsanity.com/paper/2107.09099/) (2021)
- [SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning](https://mlsanity.com/paper/2201.12528/) (2022)
- [Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19](https://mlsanity.com/paper/2012.03663/) (2020)
- [Combined tract segmentation and orientation mapping for bundle-specific tractography](https://mlsanity.com/paper/1901.10271/) (2019)
- [Supervised Contrastive Learning for Accented Speech Recognition](https://mlsanity.com/paper/2107.00921/) (2021)
- [Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections](https://mlsanity.com/paper/2011.12865/) (2020)
- [DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks](https://mlsanity.com/paper/1906.03051/) (2019)
- [High-fidelity, accelerated whole-brain submillimeter in-vivo diffusion MRI using gSlider-Spherical Ridgelets (gSlider-SR)](https://mlsanity.com/paper/1909.07925/) (2019)
- [Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions](https://mlsanity.com/paper/1904.03328/) (2019)

## Built on by (most cited first, 20 of 20)

- [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)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography](https://mlsanity.com/paper/2301.01911/) (2023)
- [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)
- [FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training](https://mlsanity.com/paper/2407.04396/) (2024)
- [TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View](https://mlsanity.com/paper/2502.20637/) (2025)
- [White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging](https://mlsanity.com/paper/2407.19460/) (2024)
- [TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds](https://mlsanity.com/paper/2410.22099/) (2024)
- [MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages](https://mlsanity.com/paper/2503.06598/) (2025)
- [DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling](https://mlsanity.com/paper/2508.04568/) (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)
- [GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter](https://mlsanity.com/paper/2303.01147/) (2023)
- [A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography](https://mlsanity.com/paper/2504.18400/) (2025)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)
- [AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography](https://mlsanity.com/paper/2507.10601/) (2025)
- [A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data](https://mlsanity.com/paper/2511.18781/) (2025)
- [Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection](https://mlsanity.com/paper/2608.16039/) (2026)
- [dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions](https://mlsanity.com/paper/2610.17968/) (2026)

## Abstract (arXiv)

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage 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 adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.