  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.
