# DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography

arXiv: https://arxiv.org/abs/1812.05129 · HTML: https://mlsanity.com/paper/1812.05129/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

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

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [TractOracle: towards an anatomically-informed reward function for RL-based tractography](https://mlsanity.com/paper/2403.17845/) (2024)
- [What Matters in Reinforcement Learning for Tractography](https://mlsanity.com/paper/2305.09041/) (2023)
- [Tract-RLFormer: A Tract-Specific RL policy based Decoder-only Transformer Network](https://mlsanity.com/paper/2411.05757/) (2024)
- [Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information](https://mlsanity.com/paper/2503.03329/) (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)
- [Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI](https://mlsanity.com/paper/2306.02900/) (2023)
- [TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks](https://mlsanity.com/paper/2509.16429/) (2025)
- [TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography](https://mlsanity.com/paper/2601.13897/) (2026)
- [A foundation for systematic analysis of transformers and RNNs for tractography](https://mlsanity.com/paper/2610.01894/) (2026)

## Abstract (arXiv)

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive performance with state-of-the art classical and machine learning based tractography algorithms. We further present qualitative results of bundle-specific probabilistic tractography obtained using our method. The code is publicly available at: https://github.com/itaybenou/DeepTract.git.