论文标题
通过将深层模仿学习与最佳控制结合起来进行自主眼科手术
Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control
论文作者
论文摘要
在视网膜显微外科手术期间，需要精确的视网膜组织进行阳性手术结果。但是，由于工作区和手术期间自上而下的视图，精确的手术工具的操纵和导航仍然很困难，这限制了外科医生估计深度的能力。为了减轻这种困难，我们建议通过学习从当前的工具尖端位置预测视网膜表面上的相对目标位置来自动化工具运动任务。考虑到视网膜上的估计目标，我们产生了最佳轨迹，导致预测目标，同时施加了与安全相关的物理约束，旨在最大程度地减少组织损伤。作为一项扩展任务，我们为整个视网膜的各个点产生目标预测，以将眼睛的几何形状定位，并在估计的范围内进一步产生安全的轨迹。通过模拟和几个眼幻象的实验，我们证明了我们的框架可以允许在0.089mm以内的视网膜上的各个点导航，而XY误差为0.118mm，这比人类的外科医生在0.180mm的工具角上的平均震颤少。所有的安全限制都得到了满足，并且算法对于以前看不见的眼睛以及现场看不见的物体都很强大。实时视频演示可在此处提供：https：//youtu.be/n5j5jccelxk
During retinal microsurgery, precise manipulation of the delicate retinal tissue is required for positive surgical outcome. However, accurate manipulation and navigation of surgical tools remain difficult due to a constrained workspace and the top-down view during the surgery, which limits the surgeon's ability to estimate depth. To alleviate such difficulty, we propose to automate the tool-navigation task by learning to predict relative goal position on the retinal surface from the current tool-tip position. Given an estimated target on the retina, we generate an optimal trajectory leading to the predicted goal while imposing safety-related physical constraints aimed to minimize tissue damage. As an extended task, we generate goal predictions to various points across the retina to localize eye geometry and further generate safe trajectories within the estimated confines. Through experiments in both simulation and with several eye phantoms, we demonstrate that our framework can permit navigation to various points on the retina within 0.089mm and 0.118mm in xy error which is less than the human's surgeon mean tremor at the tool-tip of 0.180mm. All safety constraints were fulfilled and the algorithm was robust to previously unseen eyes as well as unseen objects in the scene. Live video demonstration is available here: https://youtu.be/n5j5jCCelXk
