Statistical Shape Models for Grasp Point Determination in Laparoscopic Surgeries

Abstract

Robotic assistance systems are being used more and more frequently in the operating room, with the goal to support surgeons and to automate parts of a procedure. The laparoscopic cholecystectomy is one of the most common procedures in Germany.We aim to automate the assistant grasp task in this procedure. To achieve this goal, first the grasp points on the gallbladder need to be determined. In this work, we therefore present a statistical shape model fitting to the gallbladder for grasp point determination. Gallbladder and liver point clouds are utilized as inputs. A registration algorithm is used to fit the shape model to the gallbladder mesh. The process is evaluated on three different datasets achieving a successful grasping point identification of 90% for artificially created gallbladders, 100% for our silicon phantom model, and 90% for ex-vivo organs.

Keywords

• grasp point determination
• statistical shape models
• cholecystectomy
• robot-assisted surgery