# Tractography and machine learning: Current state and open challenges

## TL;DR

This paper reviews the current state of machine learning-based tractography, highlighting its potential advantages, existing challenges, and the need for standardized evaluation frameworks to advance the field.

## Contribution

It provides a comprehensive overview of datasets, evaluation tools, and strategies for ML tractography, and discusses future directions and solutions for current challenges.

## Key findings

- ML-based tractography shows promise in larger and more accurate white matter reconstructions
- Current ML methods lack conclusive performance and widespread adoption
- The paper identifies the need for standardized evaluation frameworks

## Abstract

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.