Depth Estimation of Hard Inclusions in Soft Tissue by Autonomous Robotic Palpation Using Deep Recurrent Neural Network

• Published in: IEEE transactions on automation science and engineering 2020-10, Vol.17 (4), p.1791-1799
• Main Authors: Xiao, Bo, Xu, Wenjun, Guo, Jing, Lam, Hak-Keung, Jia, Guangyu, Hong, Wuzhou, Ren, Hongliang
• Format: Article
• Language: English
• Subjects: Accuracy; Datasets; Deep recurrent neural network (DRNN); Displacement; Estimation; force sensor; Force sensors; Inclusions; Laparoscopy; long short-term memory (LSTM); Manipulators; Neural networks; Phantoms; Probes; Recurrent neural networks; Robot arms; Robot sensing systems; robotic palpation; Robotic surgery; Robotics; Robots; Sensors; Sensory feedback; Soft tissues; tactile sensing; Tactile sensors (robotics); Touch; tumor depth estimation; Tumors
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2020.2978881

Accurately detecting tumors and estimating the depth of tumors is essential in the surgical removal of tumors. In robotic-assisted surgery, autonomous robotic palpation has the potential to provide more precise detection, tumors' depth estimation, and less intrusion when normal tissues surround tumors. In this article, by mimicking the human finger touch, we propose a tactile sensing-based deep recurrent neural network (DRNN) with long short-term memory (LSTM) architecture to improve the accuracy of the detection and depth estimation of tumors embedded in soft tissue. In the experimental setup, the hard inclusions simulate the tumors, while the phantom tissue is fabricated by silicon to simulate the soft tissue. During the experiment, the data from the force sensor and displacement of the robot palpation probe are for detection and depth estimation purposes. The collected sequential data set of the force and the displacement of the probe during one completed palpation process will go through the proposed DRNN network with deep LSTM architecture, in which the temporal dependencies of the sequential data will be captured in the cell states in the deep LSTM layers. Subsequently, the softmax classifier is adopted to determine if there is any hard inclusion exists and offer the depth estimation of the hard inclusions. Experiments based on 396 real data sets demonstrate that the detection accuracy for the testing data set is 99.2% and the depth estimation accuracy for the testing data set is 95.8%. The accuracy of the proposed method is best when comparing with other widely used methods. Note to Practitioners -The palpation of tumors motivated this article in the robot-assisted surgical systems through tactile feedback. In order to mimic the human touch on the soft tissue, this article presents a deep-learning-based approach to estimate the depth of the hard inclusions in the phantom tissue through force information. The displacement of the palpation probe and the touch force during one palpation are recorded as data sequences to train the deep model, which aims to capture dynamics and long-term dependence of the palpation process. In this article, we made the first successful attempt to accurately estimate the depth of the hard inclusions buried at different locations of the phantom tissue using only force information. The proposed approach can work in different robot-assisted scenarios, such as master-slave robotic surgery. In the clinic applications, the forc