Robotic surgery promises enhanced precision and adaptability over traditional surgical methods. It also offers the possibility of automating surgical interventions, resulting in reduced stress on the surgeon, better surgical outcomes, and lower costs. Cholecystectomy, the removal of the gallbladder, serves as an ideal model procedure for automation due to its distinct and well-contrasted anatomical features between the gallbladder and liver, along with standardized surgical maneuvers. Dissection is a frequently used subtask in cholecystectomy where the surgeon delivers the energy on the hook to detach the gallbladder from the liver. Hence, dissection along tissue boundaries is a good candidate for surgical automation. For the da Vinci surgical robot to perform the same procedure as a surgeon automatically, it needs to have the ability to (1) recognize and distinguish between the two different tissues (e.g. the liver and the gallbladder), (2) understand where the boundary between the two tissues is located in the 3D workspace, (3) locate the instrument tip relative to the boundary in the 3D space using visual feedback, and (4) move the instrument along the boundary. This paper presents a novel framework that addresses these challenges through AI-assisted image processing and vision-based robot control. We also present the ex-vivo evaluation of the automated procedure on chicken and pork liver specimens that demonstrates the effectiveness of the proposed framework.

翻译：机器人手术相比传统手术方法具有更高的精度和适应性，同时有望实现手术操作自动化，从而减轻外科医生压力、改善手术效果并降低成本。胆囊切除术因其胆囊与肝脏之间清晰对比的解剖特征及标准化操作流程，成为实现自动化的理想模型。剥离是胆囊切除术中频繁使用的子任务，医生通过电钩施加能量将胆囊从肝脏上分离。因此，沿组织边界的剥离是手术自动化的理想候选方案。为实现达芬奇手术机器人自动执行与外科医生相同的手术操作，需具备以下能力：（1）识别并区分两种不同组织（如肝脏与胆囊）；（2）理解两种组织边界在三维工作空间中的位置；（3）通过视觉反馈定位器械尖端相对于边界的空间位置；（4）沿边界移动器械。本文提出了一种创新框架，通过人工智能辅助图像处理与基于视觉的机器人控制技术解决上述挑战。我们还在鸡肝和猪肝标本上开展了该自动化流程的离体评估，验证了所提框架的有效性。