Landmark-Guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection

Abstract

Oral squamous cell carcinoma (OSCC) is the most common cancer in the head and neck region, and is associated with high morbidity and mortality rates. Surgical resection is usually the primary treatment strategy for OSCC, and maintaining effective tumor resection margins is paramount to surgical outcomes. In practice, wide tumor excisions impair post-surgical organ function, while narrow resection margins are associated with tumor recurrence. Identification and tracking of these resection margins remain a challenge because they migrate and shrink from preoperative chemo or radiation therapies, and deform intra-operatively. This paper reports a novel near-infrared (NIR) fluorescent marking and landmark-based deformable image registration (DIR) method to precisely predict deformed margins. The accuracy of DIR predicted resection margins on porcine cadaver tongues is compared with rigid image registration and surgeon’s manual prediction. Furthermore, our tracking and registration technique is integrated into a robotic system, and tested using ex vivo porcine cadaver tongues to demonstrate the feasibility of supervised autonomous tumor bed resections.