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：机器人手术相较于传统手术方法有望实现更高的精度与适应性，同时为手术干预的自动化提供了可能，从而减轻外科医生负担、改善手术效果并降低成本。胆囊切除术（胆囊摘除）因其胆囊与肝脏之间清晰对比的解剖特征以及标准化的手术操作，成为自动化的理想模型术式。剥离是胆囊切除术中常用的子任务，医生通过电钩传递能量将胆囊从肝脏分离。因此，沿组织边界的剥离是手术自动化的理想候选方向。为使达芬奇手术机器人能自动执行与外科医生相同的操作，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）利用视觉反馈在三维空间中定位器械尖端相对于组织边界的位置；（4）沿边界移动器械。本文提出了一种新型框架，通过AI辅助图像处理与基于视觉的机器人控制解决上述挑战。我们还在鸡肝与猪肝标本上开展了自动剥离程序的离体评估，验证了所提框架的有效性。