Towards Deep Learning Guided Autonomous Eye Surgery Using Microscope and iOCT Images (2306.10127v3)

Abstract: Recent advancements in retinal surgery have paved the way for a modern operating room equipped with a surgical robot, a microscope, and intraoperative optical coherence tomography (iOCT)- a depth sensor widely used in retinal surgery. Integrating these tools raises the fundamental question of how to effectively combine them to enable surgical autonomy. In this work, we tackle this question by developing a unified framework that facilitates real-time autonomous surgical workflows leveraging these devices. The system features: (1) a novel imaging system that integrates the microscope and iOCT in real-time by dynamically tracking the surgical instrument via a small iOCT scanning region, providing real-time depth feedback; (2) implementation of convolutional neural networks (CNN) that automatically detect and segment task-relevant information for surgical autonomy; (3) intuitive selection of goal waypoints within both the microscope and iOCT views through simple mouse-click interactions; and (4) integration of model predictive control (MPC) for trajectory generation, ensuring patient safety by implementing safety-related kinematic constraints. The system's utility is demonstrated by automating subretinal injection (SI), a challenging procedure with high accuracy and depth perception requirements. We validate our system by conducting 30 successful SI trials on pig eyes, achieving mean needle insertion accuracy of 26 micrometers to various subretinal goals and mean duration of 55 seconds. Preliminary comparisons to a human operator performing SI in robot-assisted mode highlight the enhanced safety of our system. Project website is here: https://sites.google.com/view/eyesurgerymicroscopeoct/home