Label-Free Deep-Tissue Peripheral Nerve Detection with a Handheld Multimodal OCT Probe and NerveDetNet

Abstract

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.