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A fundamental challenge in retinal surgery is safely navigating a surgicaltool to a desired goal position on the retinal surface while avoiding damage tosurrounding tissues, a procedure that typically requires tens-of-micronsaccuracy. In practice, the surgeon relies on depth-estimation skills tolocalize the tool-tip with respect to the retina in order to perform thetool-navigation task, which can be prone to human error. To alleviate suchuncertainty, prior work has introduced ways to assist the surgeon by estimatingthe tool-tip distance to the retina and providing haptic or auditory feedback.However, automating the tool-navigation task itself remains unsolved andlargely unexplored. Such a capability, if reliably automated, could serve as abuilding block to streamline complex procedures and reduce the chance fortissue damage. Towards this end, we propose to automate the tool-navigationtask by learning to mimic expert demonstrations of the task. Specifically, adeep network is trained to imitate expert trajectories toward various locationson the retina based on recorded visual servoing to a given goal specified bythe user. The proposed autonomous navigation system is evaluated in simulationand in physical experiments using a silicone eye phantom. We show that thenetwork can reliably navigate a needle surgical tool to various desiredlocations within 137 microns accuracy in physical experiments and 94 microns insimulation on average, and generalizes well to unseen situations such as in thepresence of auxiliary surgical tools, variable eye backgrounds, and brightnessconditions.

Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration