TY - GEN
T1 - Using Bayesian optimization to guide probing of a flexible environment for simultaneous registration and stiffness mapping
AU - Ayvali, Elif
AU - Srivatsan, Rangaprasad Arun
AU - Wang, Long
AU - Roy, Rajarshi
AU - Simaan, Nabil
AU - Choset, Howie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - One of the goals of computer-aided surgery is to register intraoperative data to preoperative model of the anatomy, and hence add complementary information that can facilitate the task of surgical navigation. In this context, mechanical palpation can reveal critical anatomical features such as arteries and cancerous lumps which are stiffer than the surrounding tissue. This work uses position and force measurements obtained during mechanical palpation for registration and stiffness mapping. Prior approaches, including our own, exhaustively palpated the entire organ to achieve this goal. To overcome the costly palpation of the entire organ, a Bayesian optimization framework is introduced to guide the end effector to palpate stiff regions while simultaneously updating the registration of the end effector to an a priori geometric model of the organ, hence enabling the fusion of intraoperative data into the a priori model obtained through imaging. This new framework uses Gaussian processes to model the stiffness distribution and Bayesian optimization to direct where to sample next for maximum information gain. The proposed method was evaluated with experimental data obtained using a Cartesian robot interacting with a silicone organ model and an ex vivo porcine liver.
AB - One of the goals of computer-aided surgery is to register intraoperative data to preoperative model of the anatomy, and hence add complementary information that can facilitate the task of surgical navigation. In this context, mechanical palpation can reveal critical anatomical features such as arteries and cancerous lumps which are stiffer than the surrounding tissue. This work uses position and force measurements obtained during mechanical palpation for registration and stiffness mapping. Prior approaches, including our own, exhaustively palpated the entire organ to achieve this goal. To overcome the costly palpation of the entire organ, a Bayesian optimization framework is introduced to guide the end effector to palpate stiff regions while simultaneously updating the registration of the end effector to an a priori geometric model of the organ, hence enabling the fusion of intraoperative data into the a priori model obtained through imaging. This new framework uses Gaussian processes to model the stiffness distribution and Bayesian optimization to direct where to sample next for maximum information gain. The proposed method was evaluated with experimental data obtained using a Cartesian robot interacting with a silicone organ model and an ex vivo porcine liver.
UR - http://www.scopus.com/inward/record.url?scp=84977508525&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977508525&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2016.7487225
DO - 10.1109/ICRA.2016.7487225
M3 - Conference contribution
AN - SCOPUS:84977508525
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 931
EP - 936
BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Y2 - 16 May 2016 through 21 May 2016
ER -