Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

In surgical tumor removal, inaccurate localization can lead to removal of excessive healthy tissue and failure to completely remove cancerous tissue. Automated palpation with a tactile sensor has the potential to precisely estimate the geometry of embedded tumors during robot-assisted minimally invasive surgery (RMIS). We formulate tumor boundary localization as a Bayesian optimization model along implicit curves over estimated tissue stiffness. We propose a Gaussian Process Adaptive Sampling algorithm called Implicit Level Set Upper Confidence Bound (ILS-UCB), that prioritizes sampling near a level set of the estimate. We compare the ILS-UCB algorithm to two alternative palpation algorithms: (1) Expected Variance Reduction (EVR), which emphasizes exploration by minimizing variance, and (2) Upper Confidence Bound (UCB), which balances exploration with exploitation using only the estimated mean. We compare these algorithms in simulated experiments varying the levels of measurement noise and bias. We find that ILS-UCB significantly outperforms the other two algorithms as measured by the symmetric difference between tumor boundary estimate and ground truth, reducing error by up to 10×. Physical experiments on a dVRK show that our approach can localize the tumor boundary with approximately the same accuracy as a dense raster scan while requiring at least 10× fewer measurements.