Think deep in the tractography game: deep learning for tractography computing and analysis.
Authors
Zhang F,Théberge A,Jodoin PM,Descoteaux M,O'Donnell LJ
Affiliations (3)
Affiliations (3)
- University of Electronic Science and Technology of China, Chengdu, China. [email protected].
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada.
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Abstract
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.
Topics
Deep LearningDiffusion Tensor ImagingBrainImage Processing, Computer-AssistedJournal ArticleReview