TumorMap：基于激光的外科手术平台，用于三维肿瘤标绘与全自动肿瘤切除
TumorMap: A Laser-based Surgical Platform for 3D Tumor Mapping and Fully-Automated Tumor Resection
AI总结：
TumorMap平台通过三激光机制与深度学习结合，实现术中三维肿瘤边界构建和亚毫米级全自动非接触肿瘤切除，并在小鼠肿瘤模型中验证了无需人工干预的自主手术能力。
摘要：
恶性实体肿瘤的外科切除关键取决于外科医生准确识别病理组织并在保留周围健康结构的同时切除肿瘤的能力。然而，构建用于后续切除的术中三维肿瘤模型面临重大挑战，原因包括缺乏高保真肿瘤重建、难以开发能够处理肿瘤诊断固有复杂性的通用组织模型，以及手术过程中双手操作、生理性震颤和疲劳累积所带来的自然物理限制。为克服这些挑战，我们提出了“TumorMap”，这是一个外科机器人平台，用于构建术中三维肿瘤边界，并利用一组多功能激光实现自主组织切除。TumorMap集成了三激光机制（光学相干断层扫描、激光诱导内源荧光和切割激光手术刀），并结合深度学习模型，实现全自动、非接触式肿瘤切除。我们在小鼠骨肉瘤和软组织肉瘤肿瘤模型中验证了TumorMap，并建立了一种新的组织病理学工作流程来评估传感器性能。凭借亚毫米级激光切除精度，我们展示了多模态传感器引导的自主肿瘤手术，全程无需任何人工干预。
英文摘要：
Surgical resection of malignant solid tumors is critically dependent on the surgeon's ability to accurately identify pathological tissue and remove the tumor while preserving surrounding healthy structures. However, building an intraoperative 3D tumor model for subsequent removal faces major challenges due to the lack of high-fidelity tumor reconstruction, difficulties in developing generalized tissue models to handle the inherent complexities of tumor diagnosis, and the natural physical limitations of bimanual operation, physiologic tremor, and fatigue creep during surgery. To overcome these challenges, we introduce "TumorMap", a surgical robotic platform to formulate intraoperative 3D tumor boundaries and achieve autonomous tissue resection using a set of multifunctional lasers. TumorMap integrates a three-laser mechanism (optical coherence tomography, laser-induced endogenous fluorescence, and cutting laser scalpel) combined with deep learning models to achieve fully-automated and noncontact tumor resection. We validated TumorMap in murine osteoscarcoma and soft-tissue sarcoma tumor models, and established a novel histopathological workflow to estimate sensor performance. With submillimeter laser resection accuracy, we demonstrated multimodal sensor-guided autonomous tumor surgery without any human intervention.
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