Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery

Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required, they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has, however, not yet been fully exploited. In order to improve the robot control, we are interested in three-dimensional (3-D) reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this letter, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3-D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are 1) surgical tool landmark detection, and 2) 3-D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high-definition images at higher than real-time speed. We use the detected two-dimensional keypoints along with their corresponding 3-D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3-D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps.