用单目显微镜图像和位姿数据，实现神经外科手术场景的毫米级三维重建。
Metric Surface Reconstruction of Neurosurgical Scenes from Monocular Operating Microscope Images and Microscope Pose
- 结合显微镜图像与位姿信息，直接使用预训练深度模型估算深度。
- 重建精度达1.02~2.33毫米，深层通道区域误差略高。
- 为手术空间量化与智能器械开发提供新方法，适合医疗影像研究者。
目的：评估能否从标准单目手术显微镜图像与显微镜位姿数据中恢复神经外科术野的度量三维几何结构。方法：在基于假体的实验室研究中，使用集成有Brainlab颅脑导航系统的ZEISS Pentero 800显微镜拍摄两个动脉瘤训练假体。从标准复合视频输出获取显微镜图像，并同步记录显微镜位姿。经内参与外参标定后，采用未进行任务微调的预训练Depth Anything 3模型估计深度，通过泊松表面重建生成点云网格。重建结果与结构光扫描及细切层CT获取的参考表面进行对比。结果：对于代表深部术野的假体A，重建精度为1.95 ± 1.70 mm至2.33 ± 2.15 mm；对于表浅暴露面的假体B，精度为1.02 ± 0.93 mm至1.52 ± 1.21 mm。图像集越大主要提升完整性，精度范围保持稳定。通道分析显示整体几何结构保留良好，局部重建不完整区域存在偏差。结论：利用基础模型管道，仅凭标准单目显微镜图像与导航位姿数据，即可实现毫米级3D表面重建。该结果在受控假体环境中验证了技术可行性，支持未来向术野客观量化、图像融合及工作空间表征等方向发展。
原文摘要 · Abstract (English)
Objective: We evaluated whether metric 3D geometry of neurosurgical operative exposure can be recovered from standard monocular operating-microscope images combined with microscope pose data. Methods: In a phantom-based laboratory study, two aneurysm training phantoms were imaged with a ZEISS Pentero 800 microscope integrated with Brainlab Cranial Navigation. Microscope images from the standard composite video output were stored with synchronous microscope poses. After intrinsic and extrinsic calibration, depth was estimated with the pretrained Depth Anything 3 model without task-specific fine-tuning. Fused point clouds were converted to meshes using Poisson surface reconstruction. Reconstructions were compared with reference surfaces from structured-light scanning and fine-slice CT. Results: For phantom A, representing a deeper surgical corridor, reconstruction accuracy ranged from 1.95 $\pm$ 1.70 mm to 2.33 $\pm$ 2.15 mm. For phantom B, representing a directly exposed surface, accuracy ranged from 1.02 $\pm$ 0.93 mm to 1.52 $\pm$ 1.21 mm. Larger image sets mainly improved completeness, while accuracy remained within a narrower range. Corridor analysis showed preservation of overall geometry with local deviations in incompletely reconstructed regions. Conclusions: Standard monocular microscope images combined with navigation-derived pose data can reconstruct millimeter-range 3D surfaces using a foundation-model-based pipeline. These results show technical feasibility in a controlled phantom setting and support further development toward objective quantification of operative exposure, image fusion, and characterization of working spaces for future surgical instrumentation.
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