Hyperspectral Imaging (HSI) is a promising modality for intraoperative assessment of resection margins in Breast-Conserving Surgery (BCS), but its clinical translation requires aligning the inherently 2D spectral information onto the 3D shape of the excised tissue so that suspicious regions can be precisely localized for targeted follow-up. We present a fully automated, calibration-free pipeline that produces a 3D hyperspectral point cloud of an ex-vivo lumpectomy specimen from a set of consumer-camera RGB images and a single top-down HSI acquisition. The 3D geometry is reconstructed with a deep-learning Structure-from-Motion backbone, stabilized in a metric reference frame by a custom bundle adjustment that enforces consistency on the corners of four ArUco markers placed around the specimen. The HSI cube is then registered to the reconstruction without recovering the HSI camera pose: the markers, visible in both modalities, define 16 corner correspondences that drive a planar homography, and 3D coordinates are recovered by lookup on an orthographically rendered depth map. Evaluated on two ex-vivo lumpectomy specimens, the pipeline achieves a median 3D registration error below 1~mm and a 2D reprojection error below 0.02 mm, with a total per-specimen processing time under 4 minutes on accelerated hardware. These results support the feasibility of integrating HSI-guided spatial localization into intraoperative margin assessment workflows for breast-conserving surgery.

翻译：高光谱成像（HSI）是保乳手术（BCS）中实现切缘术中评估的一种有前景的技术。然而，其临床应用要求将固有的二维光谱信息与切除组织的三维形态对齐，以便对可疑区域进行精确定位以进行靶向随访。我们提出了一种全自动、免标定的管线，能够通过一组消费级相机RGB图像和单次俯视HSI采集，生成离体保乳标本的三维高光谱点云。三维几何结构通过深度学习运动恢复结构（Structure-from-Motion）主干网络重建，并利用自定义光束法平差（bundle adjustment）在公制参考系中稳定化，该平差方法通过约束放置在标本周围的四个ArUco标记的角点一致性来实现。随后，无需恢复HSI相机位姿，即可将HSI数据立方体配准至重建模型：两种模态下均可见的标记定义了16个角点对应关系，驱动平面单应性变换，并通过在正交投影深度图上查找恢复三维坐标。在两种离体保乳标本上的评估结果表明，该管线在加速硬件上的处理时间低至每标本4分钟，中位三维配准误差低于1毫米，二维重投影误差低于0.02毫米。这些结果支持将HSI引导的空间定位整合至保乳手术术中切缘评估工作流程的可行性。