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

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.

SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence

We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertise, SpatialLLM is a unified language model directly addressing various spatial intelligence tasks without any training, fine-tuning, or expert intervention. The core of SpatialLLM lies in constructing detailed and structured scene descriptions from raw spatial data to prompt pre-trained LLMs for scene-based analysis. Extensive experiments show that, with our designs, pretrained LLMs can accurately perceive spatial distribution information and enable zero-shot execution of advanced spatial intelligence tasks, including urban planning, ecological analysis, traffic management, etc. We argue that multi-field knowledge, context length, and reasoning ability are key factors influencing LLM performances in urban analysis. We hope that SpatialLLM will provide a novel viable perspective for urban intelligent analysis and management. The code and dataset are available at https://github.com/WHU-USI3DV/SpatialLLM.