Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing

TL;DR

This paper presents an autonomous robotic system for subretinal injection that uses real-time iOCT volume slicing and CNN-based virtual B-scans for precise instrument navigation in retinal surgery.

Contribution

It introduces an innovative framework combining real-time iOCT processing, CNN-based virtual B-scans, and trajectory planning for autonomous retinal surgery navigation.

Findings

Demonstrated high precision and repeatability in ex-vivo porcine eye experiments.

Developed a real-time volume slicing approach for rapid instrument pose estimation.

Identified key challenges and potential solutions for robotic retinal surgery autonomy.

Abstract

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems.