Fan Zhang, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Lauren J O'Donnell
{"title":"深入思考轨道图游戏:轨道图计算和分析的深度学习。","authors":"Fan Zhang, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Lauren J O'Donnell","doi":"10.1007/s00429-025-02938-0","DOIUrl":null,"url":null,"abstract":"<p><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>","PeriodicalId":9145,"journal":{"name":"Brain Structure & Function","volume":"230 6","pages":"100"},"PeriodicalIF":2.7000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Think deep in the tractography game: deep learning for tractography computing and analysis.\",\"authors\":\"Fan Zhang, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Lauren J O'Donnell\",\"doi\":\"10.1007/s00429-025-02938-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><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>\",\"PeriodicalId\":9145,\"journal\":{\"name\":\"Brain Structure & Function\",\"volume\":\"230 6\",\"pages\":\"100\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Structure & Function\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00429-025-02938-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANATOMY & MORPHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Structure & Function","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00429-025-02938-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANATOMY & MORPHOLOGY","Score":null,"Total":0}
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 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.
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.