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Tractography and machine learning: Current state and open challenges
Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ, Canada..ORCID iD: 0000-0002-0116-4352
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.ORCID iD: 0000-0002-6827-9162
Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ, Canada..
Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ, Canada..
2019 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 64, p. 37-48Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 64, p. 37-48
Keywords [en]
Diffusion MRI, Tractography, Machine learning, Benchmark
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-266206DOI: 10.1016/j.mri.2019.04.013ISI: 000502191300006PubMedID: 31078615Scopus ID: 2-s2.0-85065601103OAI: oai:DiVA.org:kth-266206DiVA, id: diva2:1383962
Note

QC 20200109

Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-09Bibliographically approved

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Jörgens, Daniel

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