Towards Realtime Estimation of a Spherical Eye Model based on a Single Fiber OCT Depth perception remains a key challenge in vitreoretinal surgery. Currently users only have the view from an overhead stereomicroscope available, but this means of visualisation is quite restrictive. Optical Coherence Tomography (OCT) has been introduced in the past and is even integrated in a number of commercial systems to provide a more detailed depth vision, even showing subsurface structures up to a few millimeters below the surface. The intraoperative use of OCT, especially in combination with robotics, is still suboptimal. At present one can get either a very slowly updating large volume scan (Cscan) or a faster but not aligned crosssectional Bscan or an even more local single point Ascan at very high update rate. In this work we propose a modelmediated approach. As the posterior eye segment can be approximated as a sphere, we propose to model the retina with this simplified sphere model, the center and radius of which can be estimated in real time. A timevarying Kalman filter is proposed here in combination with an instrumentintegrated optical fiber that provides highfrequency Ascans along the longitudinal instrument direction. The model and convergence of the model has been validated first in a simulation environment and subsequently insilico using an OCT Ascan probe mounted on a comanipulated vitreoretinal robotic system. The probe was manipulated to measure a 3D stereo lithographically printed spherical model of the posterior eye segment. The feasibility of the proposed method was demonstrated in various scenarios. For the insilico validation a 20 micrometer error and convergence speed of 2.0 seconds was found when sampling Ascans at 200Hz.

A Novel SemiAutonomous Control Framework for Retina Confocal Endomicroscopy Scanning * In this paper, a novel semiautonomous control framework is presented for enabling probebased confocal laser endomicroscopy (pCLE) scan of the retinal tissue. With pCLE, retinal layers such as nerve fiber layer (NFL) and retinal ganglion cell (RGC) can be scanned and characterized in realtime for an improved diagnosis and surgical outcome prediction. However, the limited field of view of the pCLE system and the micronscale optimal focus distance of the probe, which are in the order of physiological hand tremor, act as barriers to successful manual scan of retinal tissue.Therefore, a novel sensorless framework is proposed for realtime semiautonomous endomicroscopy scanning during retinal surgery. The framework consists of the SteadyHand Eye Robot (SHER) integrated with a pCLE system, where the motion of the probe is controlled semiautonomously. Through a hybrid motion control strategy, the system autonomously controls the confocal probe to optimize the sharpness and quality of the pCLE images, while providing the surgeon with the ability to scan the tissue in a tremorfree manner. Effectiveness of the proposed architecture is validated through experimental evaluations as well as a user study involving 9 participants. It is shown through statistical analyses that the proposed framework can reduce the work load experienced by the users in a statisticallysignificant manner, while also enhancing their performance in retaining pCLE images with optimized quality.