用强化学习提升脑白质纤维束追踪精度，避免误检。
Tract-RLFormer: A Tract-Specific RL policy based Decoder-only Transformer Network
- 分两阶段融合监督与强化学习，优化纤维束路径
- 在多个数据集上显著减少误报，提升泛化能力
- 针对特定纤维束设计，跳过传统分割步骤
纤维束追踪是神经影像学的核心技术，通过扩散MRI实现脑白质通路的精细映射，对理解脑连接与功能至关重要。然而，该技术因复杂性易产生假阳性，导致关键通路误判。现有方法多依赖精确标注的监督学习，或无需标注的强化学习。本文提出Tract-RLFormer，一种基于双阶段策略精炼的解码器仅架构网络，结合监督与强化学习，在多个数据集（TractoInferno、HCP、ISMRM-2015）上验证，显著提升追踪准确率与跨数据集泛化能力。通过针对特定纤维束的设计，直接勾勒目标通路，省去传统分割流程，实现更精准的脑白质结构建模。
原文摘要 · Abstract (English)
Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurological applications. Despite its importance, tractography faces challenges due to its complexity and susceptibility to false positives, misrepresenting vital pathways. To address these issues, recent strategies have shifted towards deep learning, utilizing supervised learning, which depends on precise ground truth, or reinforcement learning, which operates without it. In this work, we propose Tract-RLFormer, a network utilizing both supervised and reinforcement learning, in a two-stage policy refinement process that markedly improves the accuracy and generalizability across various data-sets. By employing a tract-specific approach, our network directly delineates the tracts of interest, bypassing the traditional segmentation process. Through rigorous validation on datasets such as TractoInferno, HCP, and ISMRM-2015, our methodology demonstrates a leap forward in tractography, showcasing its ability to accurately map the brain's white matter tracts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。