Statistical Shape Models for Grasp Point Determination in Laparoscopic Surgeries

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 la-
paroscopic cholecystectomy is one of the most common proce-
dures 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 there-
fore present a statistical shape model fitting to the gallblad-
der 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 success-
ful grasping point identification of 90% for artificially created
gallbladders, 100% for our silicon phantom model, and 90%
for ex-vivo organs.