机器人辅助手术中基于立体深度的可迁移器械-组织接触检测
Transferable Tool-Tissue Contact Detection from Stereo Depth in Robot-Assisted Surgery
- Vanderbilt University（范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于立体深度图像的器械-组织接触检测方法，采用隐马尔可夫模型，在多类预留会话上取得高泛化性能，优于RGB-based方法，验证了器械-组织距离作为可迁移线索的有效性。
AI中文摘要：
可靠的器械-组织接触检测可支持机器人辅助手术中感知交互的控制及下游力估计。现有多数方法从RGB外观学习接触分类器，泛化性较差。本研究采用立体对生成的深度图像提供器械-组织接触的更多信息：对每个深度帧，在器械边界周围定位空间支持的最小距离块，将其简化为单个标量$-\text{log}_{10}|d|$，该信号随真实接触同步升降。我们用完全监督的两状态隐马尔可夫模型形式化该观察结果，以六折留一会话（LOSO）集成在6次触诊会话（针对单个硅胶杯状体模）上拟合模型，决策阈值从合并的折外预测中选取。在4次预留会话（分三类：1. 相同任务+相同体模；2. 相同任务+不同体模；3. 不同任务+不同体模）上评估，该模型预留宏F1达0.927，AUPRC达0.980。我们进一步与复现的现有RGB-based接触分类器对比：该RGB模型在第一类任务上性能高（F1=0.965），但在其余两类任务上性能显著降低，导致4次会话的宏F1仅为0.320。这些结果表明，器械-组织距离是机器人辅助手术中接触检测的强可迁移线索。
英文摘要：
Reliable tool--tissue contact detection can support interaction-aware control and downstream force estimation in robot-assisted surgery. Most existing methods learn a contact classifier from RGB appearance, which is hard to generalize. In this work, we use the depth image generated from a stereo pair to give more information about tool--tissue contact. For each depth frame, we localize a spatially supported minimum-distance patch around the tool boundary and reduce it to a single scalar, $-\log_{10}|d|$; this signal rises and falls in step with ground-truth contact. We formalize this observation with a fully supervised two-state hidden Markov model. We fit this model as a six-fold leave-one-session-out (LOSO) ensemble on six palpation sessions against a single silicone cup-like phantom, with the decision threshold selected from the pooled out-of-fold predictions. It is evaluated on four held-out sessions of three categories: 1. same task on same phantom; 2. same task on different phantom; 3. different task on different phantom. This model reaches held-out macro F1 $0.927$ and AUPRC $0.980$. We further compare against a reproduction of an RGB-based contact classifier from prior work. This RGB-based model achieves high performance on the first category (F1 $0.965$), but substantially lower performance on the other two, resulting in macro F1 $0.320$ across all four sessions. These results indicate that the tool--tissue distance is a strong, transferable cue for contact detection in robot-assisted surgery.
