Think deep in the tractography game: deep learning for tractography computing and analysis

Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophisticated robots. Given its transformative impact in the...

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Bibliographic Details
Published in:Brain Structure and Function Vol. 230; no. 6; p. 100
Main Authors: Zhang, Fan, Théberge, Antoine, Jodoin, Pierre-Marc, Descoteaux, Maxime, O’Donnell, Lauren J.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 16.06.2025
Springer Nature B.V
Subjects:
Algorithms
Biomedical and Life Sciences
Biomedicine
Brain - diagnostic imaging
Brain research
Cell Biology
Datasets
Deep Learning
Diffusion Tensor Imaging - methods
Disease
Humans
Image Processing, Computer-Assisted - methods
Neural networks
Neurology
Neurosciences
Review
Deep learning
Diffusion MRI
Supervised vs unsupervised learning
Tractography
ISSN:1863-2661, 1863-2653, 1863-2661, 0340-2061
Online Access:Get full text
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Summary:Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophisticated robots. Given its transformative impact in these areas, deep learning has the potential to revolutionize tractography within the framework of existing rules. This work provides a brief summary of recent advances and challenges in deep learning-based tractography computing and analysis.
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ISSN:1863-2661
1863-2653
1863-2661
0340-2061
DOI:10.1007/s00429-025-02938-0