SPEVA: Stiffness Prediction from Environmental Visual Variation for Autonomous Robot-Assisted Soft Tissue Manipulation

Autonomous Robotic Surgery (ARS) is gaining traction for its potential to ease surgeon workload, enhance surgical consistency, and improve patient outcomes. The accurate perception of the surgical environment, particularly soft tissue deformation, is critical for ARS success. However, existing depth estimation techniques fall short by focusing solely on the distance to the target surface, neglecting post-contact soft tissue deformation. In response to this challenge, a novel approach has been proposed, centered on predicting tissue stiffness to address motion errors arising from soft tissue movement after contact in endoscopic robot systems. In liver puncture scenarios, a network featuring two encoders is employed to predict puncture needle force and movement information based on endoscopic images. Utilizing Hooke’s law, the tissue stiffness is calculated from the predicted data. Subsequently, this forecasted information is utilized to determine the displacement at the targeted operative site resulting from tissue elastic deformation and movement during tissue-instrument interaction. Preliminary experimental results affirm the efficacy of the proposed method in predicting stiffness, demonstrating its potential for facilitating precise tissue-instrument interaction. These results highlight the method’s promise for future applications in the field of ARS.