# TractSeg - Fast and accurate white matter tract segmentation

arXiv: https://arxiv.org/abs/1805.07103 · HTML: https://mlsanity.com/paper/1805.07103/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 30 of 52)

- [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 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)
- [Fast and Scalable Optimal Transport for Brain Tractograms](https://mlsanity.com/paper/2107.02010/) (2021)
- [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)
- [Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods](https://mlsanity.com/paper/2306.05623/) (2023)
- [One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation](https://mlsanity.com/paper/2303.06852/) (2023)
- [Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models](https://mlsanity.com/paper/2405.03652/) (2024)
- [FIESTA: Autoencoders for accurate fiber segmentation in tractography](https://mlsanity.com/paper/2212.00143/) (2022)
- [A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject](https://mlsanity.com/paper/2303.14371/) (2023)
- [FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep model](https://mlsanity.com/paper/2402.11775/) (2024)
- [A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2411.01859/) (2024)
- [Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts](https://mlsanity.com/paper/2105.14513/) (2021)
- [atTRACTive: Semi-automatic white matter tract segmentation using active learning](https://mlsanity.com/paper/2305.18905/) (2023)
- [Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture](https://mlsanity.com/paper/2307.15273/) (2023)
- [Detailed delineation of the fetal brain in diffusion MRI via multi-task learning](https://mlsanity.com/paper/2409.06716/) (2024)
- [TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View](https://mlsanity.com/paper/2502.20637/) (2025)
- [TBSS++: A novel computational method for Tract-Based Spatial Statistics](https://mlsanity.com/paper/2307.05387/) (2023)
- [BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation](https://mlsanity.com/paper/2308.10958/) (2023)
- [Anatomically Constrained Tractography of the Fetal Brain](https://mlsanity.com/paper/2403.02444/) (2024)
- [Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI](https://mlsanity.com/paper/2409.13846/) (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)
- [Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering](https://mlsanity.com/paper/2205.04843/) (2022)
- [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)
- [Supervised Tractogram Filtering using Geometric Deep Learning](https://mlsanity.com/paper/2212.03300/) (2022)
- [Direct segmentation of brain white matter tracts in diffusion MRI](https://mlsanity.com/paper/2307.02223/) (2023)
- [Streamline tractography of the fetal brain in utero with machine learning](https://mlsanity.com/paper/2408.14326/) (2024)

## Abstract (arXiv)

The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines allows for the in-vivo delineation and analysis of anatomically well known tracts. This, however, currently requires complex, computationally intensive and tedious-to-set-up processing pipelines. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without requiring tractography, image registration or parcellation. We demonstrate in 105 subjects from the Human Connectome Project that the proposed approach is much faster than existing methods while providing unprecedented accuracy. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.