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Surgical automation has the potential to enable increased precision andreduce the per-patient workload of overburdened human surgeons. An effectiveautomation system must be able to sense and map subsurface anatomy, such astumors, efficiently and accurately. In this work, we present a method thatplans a sequence of sensing actions to map the 3D geometry of subsurfacetumors. We leverage a sequential Bayesian Hilbert map to create a 3Dprobabilistic occupancy model that represents the likelihood that any givenpoint in the anatomy is occupied by a tumor, conditioned on sensor readings. Weiteratively update the map, utilizing Bayesian optimization to determinesensing poses that explore unsensed regions of anatomy and exploit theknowledge gained by previous sensing actions. We demonstrate our method'sefficiency and accuracy in three anatomical scenarios including a liver tumorscenario generated from a real patient's CT scan. The results show that ourproposed method significantly outperforms comparison methods in terms ofefficiency while detecting subsurface tumors with high accuracy.

Planning Sensing Sequences for Subsurface 3D Tumor Mapping