Surgical state estimators in robot-assisted surgery (RAS) - especially those trained via learning techniques - rely heavily on datasets that capture surgeon actions in laboratory or real-world surgical tasks. Real-world RAS datasets are costly to acquire, are obtained from multiple surgeons who may use different surgical strategies, and are recorded under uncontrolled conditions in highly complex environments. The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques. We propose StiseNet, a Surgical Task Invariance State Estimation Network with an invariance induction framework that minimizes the effects of variations in surgical technique and operating environments inherent to RAS datasets. StiseNet's adversarial architecture learns to separate nuisance factors from information needed for surgical state estimation. StiseNet is shown to outperform state-of-the-art state estimation methods on three datasets (including a new real-world RAS dataset: HERNIA-20).
翻译:机器人辅助外科手术(RAS)的外科状态估计器,尤其是那些通过学习技术培训的外科手术,严重依赖在实验室或现实世界外科手术任务中采集外科手术动作的数据集。真实世界的RAS数据集是昂贵的,获取成本高的,来自多个外科医生,这些外科医生可能采用不同的外科战略,在高度复杂的环境中记录在不受控制的条件下。高多样性和有限数据的结合要求采用对操作条件和外科手术技术具有强健性和不易变性的新学习方法。我们提议StiseNet,即外科工作不稳定状态动动动动动动网络,并有一个不动性诱导框架,最大限度地减少外科手术技术和外科手术环境变化的影响。StiseNet的对抗性结构学会将干扰因素与外科状态估计所需信息分开。 StiseNet显示三个数据集(包括一个新的真实世界RAS数据集:HERNIA-20)的状态估计方法优劣。