使用手持式多模态OCT探头和NerveDetNet进行无标记深层组织周围神经检测
Label-Free Deep-Tissue Peripheral Nerve Detection with a Handheld Multimodal OCT Probe and NerveDetNet
- Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）
- University of Pennsylvania School of Dental Medicine（宾夕法尼亚大学牙医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究开发了结合手持式多模态OCT探头与NerveDetNet的无标记框架，可在不切开组织的情况下检测皮下周围神经并解析深度，其性能优于多种基线方法，具备术中应用潜力。
AI中文摘要：
完整组织下方的周围神经在手术期间难以可视化，白光宽场成像及其他表面光学成像方法无法对其进行探测。现有的OCT神经研究大多依赖暴露的神经或偏振对比度，但其深度穿透能力有限，限制了其在皮下术中引导中的应用价值。在此，我们推出了据我们所知首个无标记框架，该框架仅基于强度型OCT结构特征，用于检测未切开组织下方的周围神经并解析其深度。该框架结合了手持式多模态探头（集成扫频源OCT与配准的白光及自发荧光成像）和专为实际手术使用设计的“先确认再采集”工作流程。为实现对稀疏采样OCT体积的高效分析，我们开发了NerveDetNet，这是一种轻量型2.5D分割网络，通过专用神经特征关联模块整合空间上下文、帧序信息及跨帧的平移容忍关联，以恢复微弱且空间位移的神经信号。在离体组织实验中，NerveDetNet在所有帧间距下均持续优于6种代表性2D基线方法，在最稀疏采样条件下达到0.725的Dice分数，同时模型参数规模约为基线方法的一半。端到端验证显示，该方法可定位表面不可见的神经，且能检测到组织表面下方1.3-1.4mm处的神经，OCT生成的深度图可直接叠加在手术视野上。综上，这些结果确立了一种实用的无标记皮下神经可视化方法，该方法支持术中兼容性，可实现稀疏体积的高效分析，且无需组织切开、造影剂或神经暴露即可提供深度分辨引导。
英文摘要：
Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and other surface optical imaging methods. Existing OCT nerve studies have largely relied on exposed nerves or polarization contrast with limited depth penetration, restricting their value for subsurface intraoperative guidance. Here, we introduce, to our knowledge, the first label-free framework for detecting peripheral nerves beneath unopened tissue and resolving their depth using intensity-based OCT structural signatures alone. The framework combines a handheld multimodal probe, integrating swept-source OCT with co-registered white light and autofluorescence imaging, with a ``confirm-then-capture'' workflow designed for practical surgical use. To enable efficient analysis of sparsely sampled OCT volumes, we develop NerveDetNet, a lightweight 2.5D segmentation network that recovers weak and spatially displaced nerve signals by incorporating spatial context, frame-order information, and shift-tolerant correlations across frames through a dedicated nerve feature correlation module. In ex vivo tissue experiments, NerveDetNet consistently outperformed six representative 2D baselines across all frame spacings, achieving a Dice score of 0.725 under the sparsest sampling condition while using approximately half the model parameters. End-to-end validation demonstrated localization of nerves invisible at the surface and depth-resolved detection up to 1.3--1.4~mm below the tissue surface, with OCT derived depth maps overlaid directly onto the surgical view. Together, these results establish a practical label-free approach for subsurface nerve visualization that supports intraoperative compatibility, enables efficient sparse-volume analysis, and provides depth-resolved guidance without tissue opening, contrast agents, or nerve exposure.
