Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as retina penetration in subretinal injection and removal of floating tissues in retinal detachment depending on the input imaging modalities such as microscopy and intraoperative OCT (iOCT). Although iOCT is explored to locate the needle tip within its range-limited ROI, it is still difficult to coordinate iOCT's motion with the needle, especially at the initial target-approaching stage. Meanwhile, due to 2D perspective projection and thus the loss of depth information, current image-based methods cannot effectively estimate the needle tip's trajectory towards both retinal and floating targets. To address this limitation, we propose to use the shadow positions of the target and the instrument tip to estimate their relative depth position and accordingly optimize the instrument tip's insertion trajectory until the tip approaches targets within iOCT's scanning area. Our method succeeds target approaching on a retina model, and achieves an average depth error of 0.0127 mm and 0.3473 mm for floating and retinal targets respectively in the surgical simulator without damaging the retina.

翻译：摘要：机器人眼科手术是一项新兴技术，旨在通过显微成像和术中光学相干断层扫描（iOCT）等输入成像模态，促进高精度干预操作，例如视网膜下注射中的视网膜穿刺和视网膜脱离中浮动组织的清除。尽管iOCT已被用于在其有限范围的感兴趣区域（ROI）内定位针尖，但协调iOCT运动与针尖运动仍面临困难，尤其是在初始目标逼近阶段。同时，由于二维透视投影导致深度信息丢失，现有基于图像的方法无法有效估计针尖朝向视网膜目标和浮动目标的轨迹。为克服这一局限，我们提出利用目标和器械尖端的阴影位置估计其相对深度位置，并据此优化器械尖端的插入轨迹，直至尖端在iOCT扫描区域内逼近目标。该方法在视网膜模型上成功实现目标逼近，并在手术模拟器中实现了对浮动目标和视网膜目标的平均深度误差分别为0.0127毫米和0.3473毫米，且未损伤视网膜。