Tractography and machine learning: Current state and open challenges

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.

References in corpus (6)

• TractSeg - Fast and accurate white matter tract segmentation
• Sample Size Planning for Classification Models
• The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy
• HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
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Cited by in corpus (6)

• Towards non-parametric fiber-specific $T_1$ relaxometry in the human brain
• Exploring the robustness of TractOracle methods in RL-based tractography
• Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review
• Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
• DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling
• Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging