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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Fan Zhang,
  • Antoine Théberge,
  • Pierre-Marc Jodoin,
  • Maxime Descoteaux,
  • Lauren J. O’Donnell

摘要

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.