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