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

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 micrometer accuracy in physical experiments and 94 micrometer 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

Development and Experimental Validation of a Combined FBG Force and OCT Distance Sensing Needle for Robot-Assisted Retinal Vein Cannulation

© 2018 IEEE. Retinal Vein Occlusion is a common retinal vascular disorder which can cause severe loss of vision. Retinal vein cannulation followed by injection of an anti-coagulant into the affected vein is a promising treatment. However, given the scale and fragility of the surgical workfield, this procedure is considered too high-risk to perform manually. A first successful robot-assisted procedure has been demonstrated. Even though successful, the procedure remains extremely challenging. This paper aims at providing a solution for the limited perception of instrument-tissue interaction forces as well as depth estimation during retinal vein cannulation. The development of a novel combined force and distance sensing cannulation needle relying on Fiber Bragg grating (FBG) and Optical Coherence Tomography (OCT) A-scan technology is reported. The design, the manufacturing process, the calibration method, and the experimental characterization of the produced sensor are discussed. The functionality of the combined sensing modalities and the real-time distance estimation algorithm are validated respectively on in-vitro and ex-vivo models.status: publishe