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.
翻译:外科自动化具有提高精确度和减少负担过重的人类外科外科医生每病人工作量的潜力。有效的自动化系统必须能够高效和准确地感知和绘制表层下解剖学,例如肿瘤。在这项工作中,我们提出了一个方法,计划一系列遥感行动来绘制表层下肿瘤的3D几何图。我们利用一个相继的Bayesian Hilbert地图来制作一个3D概率占用模型,该模型代表了以感官读数为条件的剖析术中任何特定点被肿瘤占据的可能性。我们反复更新了地图,利用Bayesian优化来测定探测未监测的解剖学区域并利用以往感测行动所获得的知识的感测学成分。我们用三种解剖学情景展示了我们的方法的效率和准确性,包括从真正的病人CT扫描中产生的肝肿瘤情景。结果显示,我们拟议的方法在效率方面大大优于比较方法,同时以高精度探测表层下肿瘤。