Self-organizing maps for brain tractography in MRI

Duru, Dilek Goksel; Ozkan, Mehmed

doi:10.1109/ner.2013.6696232

Cited by 1 publication

(1 citation statement)

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“…To address these inherent difficulties, recent proposals suggest that machine learning (ML) algorithms, supervised or unsupervised, may be used to implicitly learn a local, global or contextual fiber orientation model as well as the tracking procedure. Approaches ranging from the application of self-organizing maps (SOM) [19,20], random forests (RF) [18,21], Multilayer Perceptrons (MLP) [22,23], Gated Recurrent Units (GRU) [24,25,26], as well as Convolutional Neural Networks (CNN) [27] and Autoencoders [28], have been employed at the core of tractography to drive streamline progression. Apart from the differences in their underlying architecture, these ML methods differ substantially in aspects of the exact problem formulation, e.g.…”

Section: Introductionmentioning

confidence: 99%

Tractography and machine learning: Current state and open challenges

Poulin

Jörgens

Jodoin

et al. 2019

Magnetic Resonance Imaging

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Supervised machine learning (ML) algorithms have recentlybeen 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. K E Y W O R D SDiffusion MRI, tractography, machine learning, benchmark 1 arXiv:1902.05568v1 [q-bio.NC]

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