Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery

Abstract

In this work, we develop an approach for guiding robots to automatically localize and find the shapes of tumors and other stiff inclusions present in the anatomy. Our approach uses Gaussian processes to model the stiffness distribution and active learning to direct the palpation path of the robot. The palpation paths are chosen such that they maximize an acquisition function provided by an active learning algorithm. Our approach provides the flexibility to avoid obstacles in the robot's path, incorporate uncertainties in robot position and sensor measurements, include prior information about location of stiff inclusions while respecting the robot-kinematics. To the best of our knowledge this is the first work in literature that considers all the above conditions while localizing tumors. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector Platform (IREP). Results show that our approach can accurately estimate the locations and boundaries of the stiff inclusions while reducing exploration time.

A resonant tactile stiffness sensor for lump localization in robot-assisted minimally invasive surgery

“…In recent years, some researchers are studying automatic palpation algorithms applying Gaussian process regression and other algorithms to accelerate the palpation progress. 29,30 In future work, some artificial intelligence algorithms such as reinforcement learning and Bayesian optimization algorithm will be applied to obtain accurate tumor contour with as few measurements as possible.…”

Conditioned haptic perception for 3D localization of nodules in soft tissue palpation with a variable stiffness probe

“…Since only the point where the force is maximum are used for the estimation of the hard inclusion, this strategy can be considered as a hybrid method between the sweeping method and point by point method. Salman et al proposed an algorithm for a stiff probe that computes an optimal trajectory of sweeping palpation after based on prior knowledge obtained with point by point palpation [41]. They, in particular, have shown that switching from point by point to sweeping can save examination time.…”

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

“…These methods model the stiffness map using a Gaussian process regression (GPR) and reduce the exploration time by directing the robot to stiff regions. While the objective of most prior works is to find the high stiffness regions [2], [4], [5], our recent work on active search explicitly encodes finding the location and the shape of the tumor as its objective [6].…”

“…This work has been funded through the National Robotics Initiative by NSF grant IIS-1426655. While the works in literature deal with force sensing [10], [11], tumor localization [2], [4]- [6], [12] and graphical image overlays [13]- [16], there is a gap in literature when it comes to systems that deal with all these issues at the same time. For example, Yamamoto et al [16] deal with tumor localization and visual overlay, but they assume the organ is flat and place the organ on a force sensing plate, which is not representative of a surgical scenario.…”