Robot-assisted minimally invasive surgery (RMIS) has become increasingly popular in the resection of cancers. However, the lack of tactile feedback in clinical RMIS limits the surgeon's haptic understanding of tissue mechanics, making it hard to detect tissue abnormalities (e.g., tumor) efficiently. In this letter, we propose an approach that can simultaneously localize and segment the hard inclusions (artificial tumor) in artificial tissue via autonomous robotic palpation with a tactile sensor. By using Bayesian optimization guided probing, the tumor can be quickly localized within 30 iterations of the algorithm. And by continuously sliding the sensor over the tissue surface, the boundary of the tumor can be precisely segmented from the surrounding soft tissue with a high sensitivity up to 0.999 and specificity up to 0.973. Moreover, the tumor depth can be estimated with Gaussian Process (GP) regression with the root mean squared error (RMSE) being only around 0.1 mm. Our method is proven to be robust and efficient in both simulation and experiments, which provides new insight into fast tissue abnormalities detection during RMIS and could be beneficial to relevant surgical tasks like tumor removal.

中文翻译：

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自主机器人触诊对组织异常的快速定位和分割

机器人辅助微创手术（RMIS）在癌症切除术中变得越来越流行。但是，临床RMIS中缺乏触觉反馈，限制了外科医生对组织力学的触觉理解，从而难以有效地检测组织异常（例如肿瘤）。在这封信中，我们提出了一种方法，该方法可以通过带有触觉传感器的自动机器人触诊同时定位和分割人造组织中的硬包裹体（人工肿瘤）。通过使用贝叶斯优化引导探测，可以在算法的30次迭代中快速定位肿瘤。通过在整个组织表面上连续滑动传感器，可以从周围的软组织中精确分割出肿瘤的边界，灵敏度高达0.999，特异性高达0.973。而且，肿瘤深度可以通过高斯过程（GP）回归估计，均方根误差（RMSE）仅约为0.1 mm。我们的方法在仿真和实验中均被证明是可靠且高效的，这为RMIS期间的快速组织异常检测提供了新的见识，并且可能对相关的外科手术任务（如肿瘤切除）有益。