  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.
