Planning Sensing Sequences for Subsurface 3D Tumor Mapping

TL;DR

This paper introduces a method for planning sensing sequences to efficiently and accurately map subsurface tumors in 3D using Bayesian optimization and probabilistic occupancy models, demonstrated on real patient data.

Contribution

It presents a novel approach combining Bayesian Hilbert maps and optimization for autonomous subsurface tumor mapping, improving efficiency and accuracy over existing methods.

Findings

Significantly outperforms comparison methods in efficiency.

Achieves high accuracy in detecting subsurface tumors.

Demonstrated on real patient CT scan data.

Abstract

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.