# TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation

arXiv: https://arxiv.org/abs/2307.09000 · HTML: https://mlsanity.com/paper/2307.09000/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis](https://mlsanity.com/paper/2105.01288/) (2021)
- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis](https://mlsanity.com/paper/2112.04863/) (2021)
- [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)
- [Tractogram filtering of anatomically non-plausible fibers with geometric deep learning](https://mlsanity.com/paper/2003.11013/) (2020)
- [Filtering in tractography using autoencoders (FINTA)](https://mlsanity.com/paper/2010.04007/) (2020)
- [Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation](https://mlsanity.com/paper/2107.04938/) (2021)
- [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)
- [SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning](https://mlsanity.com/paper/2201.12528/) (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)
- [White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning](https://mlsanity.com/paper/2207.02402/) (2022)
- [DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks](https://mlsanity.com/paper/1906.03051/) (2019)
- [One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation](https://mlsanity.com/paper/2303.06852/) (2023)
- [A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject](https://mlsanity.com/paper/2303.14371/) (2023)
- [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)

## Built on by (most cited first, 11 of 11)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography](https://mlsanity.com/paper/2407.08883/) (2024)
- [MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages](https://mlsanity.com/paper/2503.06598/) (2025)
- [TractoGPT: A GPT architecture for White Matter Segmentation](https://mlsanity.com/paper/2501.15464/) (2025)
- [Global Context Is All You Need for Parallel Efficient Tractography Parcellation](https://mlsanity.com/paper/2503.07104/) (2025)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)
- [A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data](https://mlsanity.com/paper/2511.18781/) (2025)
- [TrackletGPT: A Language-like GPT Framework for White Matter Tract Segmentation](https://mlsanity.com/paper/2601.13935/) (2026)
- [Tractogram foundation model](https://mlsanity.com/paper/2606.09893/) (2026)
- [TractoGraphVLM: A Unified Vision-Language Framework for White Matter Tractography](https://mlsanity.com/paper/2608.18166/) (2026)
- [FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter](https://mlsanity.com/paper/2610.02755/) (2026)

## Abstract (arXiv)

Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for large-scale datasets. Recently, deep-learning-based methods have been proposed for tractography parcellation using various types of representations for streamlines. However, these methods only focus on the information from a single streamline, ignoring geometric relationships between the streamlines in the brain. We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space. We propose a novel, learnable, local-global streamline representation that leverages information from neighboring and whole-brain streamlines to describe the local anatomy and global pose of the brain. We train our framework on a large-scale labeled tractography dataset, which we augment by applying synthetic transforms including rotation, scaling, and translations. We test our framework on five independently acquired datasets across populations and health conditions. TractCloud significantly outperforms several state-of-the-art methods on all testing datasets. TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration. The robustness and high inference speed of TractCloud make it suitable for large-scale tractography data analysis. Our project page is available at https://tractcloud.github.io/.