Uncertainty-Aware Contextual Visualization for Human Supervision of OCT-Guided Autonomous Robotic Subretinal Injection

The injection of therapeutic agents into the sub-retinal space might allow improved treatment of age-related macular degeneration. Various robotic systems have been developed to achieve the required precision and, in combination with intraoperative Optical Coherence Tomography (iOCT) imaging, methods for autonomous robotic guidance have been proposed. In such systems, the robot's cognition is often governed by machine learning algorithms, such as convolutional neural networks (CNNs), which provide semantic scene information from iOCT images. Although the robot performs a surgical task autonomously, human supervision is critical to monitor the robot's execution and, if necessary, stop the robot or take control to avoid trauma to the patient. In this paper, we propose a novel visualization concept for improved human supervision of autonomous robotic subretinal injection that integrates uncertainty information of the data provided to the robot. We design a focus and context visualization that renders an automatically identified instrument-aligned B-scan in the context of the 3D OCT volume. Our visualization is enriched by augmenting the uncertainty information on the instrument-aligned B-scan. To dynamically model task-specific uncertainty, we introduce a weighting scheme to assign an importance factor to each pair of classes, controlling the impact of their confusion on the overall uncertainty. We demonstrate our visualization concept on iOCT volumes acquired at different stages during subretinal injection on ex-vivo porcine eyes. We show that our processing pipeline achieves sufficient update rates for surgical display and discuss the impact of our visualization concept on the acceptance of robotic task autonomy for subretinal injection procedures.