  Surgical automation has the potential to enable increased precision and
reduce the per-patient workload of overburdened human surgeons. An effective
automation system must be able to sense and map subsurface anatomy, such as
tumors, efficiently and accurately. In this work, we present a method that
plans a sequence of sensing actions to map the 3D geometry of subsurface
tumors. We leverage a sequential Bayesian Hilbert map to create a 3D
probabilistic occupancy model that represents the likelihood that any given
point in the anatomy is occupied by a tumor, conditioned on sensor readings. We
iteratively update the map, utilizing Bayesian optimization to determine
sensing poses that explore unsensed regions of anatomy and exploit the
knowledge gained by previous sensing actions. We demonstrate our method's
efficiency and accuracy in three anatomical scenarios including a liver tumor
scenario generated from a real patient's CT scan. The results show that our
proposed method significantly outperforms comparison methods in terms of
efficiency while detecting subsurface tumors with high accuracy.
