# Tractography and machine learning: Current state and open challenges

arXiv: https://arxiv.org/abs/1902.05568 · HTML: https://mlsanity.com/paper/1902.05568/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Generative Sampling in Bundle Tractography using Autoencoders (GESTA)](https://mlsanity.com/paper/2204.10891/) (2022)
- [Detailed delineation of the fetal brain in diffusion MRI via multi-task learning](https://mlsanity.com/paper/2409.06716/) (2024)
- [DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling](https://mlsanity.com/paper/2508.04568/) (2025)
- [Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI](https://mlsanity.com/paper/2306.02900/) (2023)
- [Streamline tractography of the fetal brain in utero with machine learning](https://mlsanity.com/paper/2408.14326/) (2024)
- [Exploring the robustness of TractOracle methods in RL-based tractography](https://mlsanity.com/paper/2507.11486/) (2025)
- [TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks](https://mlsanity.com/paper/2509.16429/) (2025)
- [Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation](https://mlsanity.com/paper/2307.02825/) (2023)

## Abstract (arXiv)

Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-model-free, and easily incorporate anatomical priors to make contextual and non-local decisions that should help the tracking process. ML-based techniques have thus shown promising reconstructions of larger spatial extent of existing white matter bundles, promising reconstructions of less false positives, and promising robustness to known position and shape biases of current tractography techniques. But as of today, none of these ML-based methods have shown conclusive performances or have been adopted as a de facto solution to tractography. One reason for this might be the lack of well-defined and extensive frameworks to train, evaluate, and compare these methods. In this paper, we describe several datasets and evaluation tools that contain useful features for ML algorithms, along with the various methods proposed in the recent years. We then discuss the strategies that are used to evaluate and compare those methods, as well as their shortcomings. Finally, we describe the particular needs of ML tractography methods and discuss tangible solutions for future works.