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

IF 2.7 3区医学 Q1 ANATOMY & MORPHOLOGY

Brain Structure & Function Pub Date : 2025-06-16 DOI:10.1007/s00429-025-02938-0

Fan Zhang, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Lauren J O'Donnell

引用次数: 0

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.

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深入思考轨道图游戏：轨道图计算和分析的深度学习。

轨迹成像是一个具有复杂规则的具有挑战性的过程，需要不断的算法进化来应对挑战。与此同时，深度学习已经解决了类似的困难任务，比如掌握围棋和为复杂的机器人动画。鉴于其在这些领域的变革性影响，深度学习有可能在现有规则的框架内彻底改变轨迹学。这项工作简要总结了基于深度学习的轨迹图计算和分析的最新进展和挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Brain Structure & Function 医学-解剖学与形态学

CiteScore

6.00

自引率

6.50%

发文量

168

审稿时长

8 months

期刊介绍： Brain Structure & Function publishes research that provides insight into brain structure−function relationships. Studies published here integrate data spanning from molecular, cellular, developmental, and systems architecture to the neuroanatomy of behavior and cognitive functions. Manuscripts with focus on the spinal cord or the peripheral nervous system are not accepted for publication. Manuscripts with focus on diseases, animal models of diseases, or disease-related mechanisms are only considered for publication, if the findings provide novel insight into the organization and mechanisms of normal brain structure and function.