A review on learning-based algorithms for tractography and human brain white matter tracts recognition.
Purpose: Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the posit...
| Publicado en: | Neuroradiology Vol. 67; no. 8; pp. 2041 - 2068 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188477967&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188477967 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Aug2025 vid: 67 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188477967 185676196 188477967 188477967 10.1007/s00234-025-03637-7 188477967 ppf: 2041 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A review on learning-based algorithms for tractography and human brain white matter tracts recognition. aug: au: Barati shoorche, Amin Farnia, Parastoo Makkiabadi, Bahador Leemans, Alexander affil: https://ror.org/01c4pz451 Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS), Tehran, Iran sug: subj: Algorithms Magnetic Resonance Imaging White Matter Neural Pathways Anatomy and Histology Brain Pathology Machine Learning Algorithms Deep Learning Reinforcement (Psychology) Convolutional Neural Networks ab: Purpose: Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the position of neural pathways, minimizes the risk of damage during neurosurgical procedures. Methods: Both tractography itself and its post-processing steps such as bundle segmentation are usually used in these contexts. Many approaches have been put forward in the past decades and recently, multiple data-driven tractography algorithms and automatic segmentation pipelines have been proposed to address the limitations of traditional methods. Results: Several of these recent methods are based on learning algorithms that have demonstrated promising results. In this study, in addition to introducing diffusion MRI datasets, we review learning-based algorithms such as conventional machine learning, deep learning, reinforcement learning and dictionary learning methods that have been used for white matter tract, nerve and pathway recognition as well as whole brain streamlines or whole brain tractogram creation. Conclusion: The contribution is to discuss both tractography and tract recognition methods, in addition to extending previous related reviews with most recent methods, covering architectures as well as network details, assess the efficiency of learning-based methods through a comprehensive comparison in this field, and finally demonstrate the important role of learning-based methods in tractography. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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