Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images

TL;DR

This paper presents a marker-free, online hand-eye calibration framework for ophthalmic robotic surgery using OCT images, improving calibration accuracy and robustness without requiring markers, verified through ex-vivo pig eye experiments.

Contribution

The authors developed a novel marker-free calibration method using OCT images, enabling precise, online hand-eye calibration for ophthalmic robots, which is more flexible and robust than existing marker-based approaches.

Findings

Mean calibration error of 9.2 μm compared to marker-based method.

Robustness demonstrated through noise testing.

Effective in ex-vivo pig eye experiments.

Abstract

Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery (RAS) is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this work, we developed a flexible framework for hand-eye calibration of an ophthalmic robot with a microscope-integrated Optical Coherence Tomography (MIOCT) without any markers. The proposed method consists of three main steps: a) we estimate the OCT calibration parameters; b) with micro-scale displacements controlled by the robot, we detect and segment the needle tip in 3D-OCT volume; c) we find the transformation between the coordinate system of the OCT camera and the coordinate system of the robot. We verified the capability of our framework in ex-vivo pig eye experiments and compared the results with a reference method (marker-based). In all experiments, our method showed a small difference from the marker based method, with a mean calibration error of 9.2 $\mu$m and 7.0 $\mu$m, respectively. Additionally, the noise test shows the robustness of the proposed method.