  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
