We propose a general strategy for autonomous guidance and insertion of a needle into a retinal blood vessel. The main challenges underpinning this task are the accurate placement of the needle-tip on the target vein and a careful needle insertion maneuver to avoid double-puncturing the vein, while dealing with challenging kinematic constraints and depth-estimation uncertainty. Following how surgeons perform this task purely based on visual feedback, we develop a system which relies solely on \emph{monocular} visual cues by combining data-driven kinematic and contact estimation, visual-servoing, and model-based optimal control. By relying on both known kinematic models, as well as deep-learning based perception modules, the system can localize the surgical needle tip and detect needle-tissue interactions and venipuncture events. The outputs from these perception modules are then combined with a motion planning framework that uses visual-servoing and optimal control to cannulate the target vein, while respecting kinematic constraints that consider the safety of the procedure. We demonstrate that we can reliably and consistently perform needle insertion in the domain of retinal surgery, specifically in performing retinal vein cannulation. Using cadaveric pig eyes, we demonstrate that our system can navigate to target veins within 22$\mu m$ XY accuracy and perform the entire procedure in less than 35 seconds on average, and all 24 trials performed on 4 pig eyes were successful. Preliminary comparison study against a human operator show that our system is consistently more accurate and safer, especially during safety-critical needle-tissue interactions. To the best of the authors' knowledge, this work accomplishes a first demonstration of autonomous retinal vein cannulation at a clinically-relevant setting using animal tissues.

翻译：我们提出了一种将针自主引导并插入视网膜血管的通用策略。该任务的主要挑战在于：将针尖精确放置于目标静脉上，并执行谨慎的插入操作以避免静脉双重穿刺，同时处理棘手的运动学约束和深度估计不确定性。借鉴外科医生完全依赖视觉反馈执行此任务的方式，我们开发了一种仅依赖单目视觉线索的系统，该系统结合了数据驱动的运动学与接触估计、视觉伺服和基于模型的最优控制。通过依赖已知运动学模型以及基于深度学习的感知模块，系统能够定位手术针尖并检测针-组织相互作用及静脉穿刺事件。这些感知模块的输出随后与运动规划框架相结合，该框架利用视觉伺服和最优控制对目标静脉进行插管，同时考虑手术安全性的运动学约束。我们证明了能够在视网膜手术领域（特别是视网膜静脉插管）稳定且一致地执行针插入操作。使用猪尸体眼球进行的实验表明，我们的系统能以22微米的XY精度导航至目标静脉，平均在35秒内完成整个流程，且对4只猪眼球进行的24次试验全部成功。与人类操作员的初步对比研究显示，我们的系统在安全关键性针-组织交互过程中始终具有更高的准确性和安全性。据作者所知，本研究首次在临床相关环境下使用动物组织实现了自主视网膜静脉插管的验证。