Raman spectroscopy, a photonic modality based on the inelastic backscattering
of coherent light, is a valuable asset to the intraoperative sensing space,
offering non-ionizing potential and highly-specific molecular fingerprint-like
spectroscopic signatures that can be used for diagnosis of pathological tissue
in the dynamic surgical field. Though Raman suffers from weakness in intensity,
Surface-Enhanced Raman Spectroscopy (SERS), which uses metal nanostructures to
amplify Raman signals, can achieve detection sensitivities that rival
traditional photonic modalities. In this study, we outline a robotic Raman
system that can reliably pinpoint the location and boundaries of a tumor
embedded in healthy tissue, modeled here as a tissue-mimicking phantom with
selectively infused Gold Nanostar regions. Further, due to the relative dearth
of collected biological SERS or Raman data, we implement transfer learning to
achieve 100% validation classification accuracy for Gold Nanostars compared to
Control Agarose, thus providing a proof-of-concept for Raman-based deep
learning training pipelines. We reconstruct a surgical field of 30x60mm in 10.2
minutes, and achieve 98.2% accuracy, preserving relative measurements between
features in the phantom. We also achieve an 84.3% Intersection-over-Union
score, which is the extent of overlap between the ground truth and predicted
reconstructions. Lastly, we also demonstrate that the Raman system and
classification algorithm do not discern based on sample color, but instead on
presence of SERS agents. This study provides a crucial step in the translation
of intelligent Raman systems in intraoperative oncological spaces.

本研究提出了一种机器人拉曼系统，能够可靠地定位和界定嵌入到健康组织中的肿瘤，在模拟组织中建模为选择性注入金纳米星区域。此外，通过实现迁移学习，对比控制琼脂进行了金纳米星的验证分类，从而为基于拉曼的深度学习训练流程提供了概念验证。该研究在 10.2 分钟内重建了 30x60mm 的手术区域，并实现了 98.2% 的准确度。此外，还验证了拉曼系统和分类算法不是基于样本颜色，而是基于 SERS 试剂的存在。本研究在术中肿瘤学领域中智能拉曼系统的应用上迈出了关键一步。