Surgical tool tracking and pose estimation in retinal microsurgery

Retinal Microsurgery (RM) is performed with small surgical tools which are observed through a microscope. Real-time estimation of the tool’s pose enables the application of various computer-assisted techniques such as augmented reality, with the potential of improving the clinical outcome. However, most existing methods are prone to fail in in-vivo sequences due to partial occlusions, illumination and appearance changes of the tool. To overcome these problems, we propose an algorithm for simultaneous tool tracking and pose estimation that is inspired by state-of-the-art computer vision techniques. Specifically, we introduce a method based on regression forests to track the tool tip and to recover the tool’s articulated pose. To demonstrate the performance of our algorithm, we evaluate on a dataset which comprises four real surgery sequences, and compare with the state-of-the-art methods on a publicly available dataset.