Yangming Li

Improving position precision of a servo-controlled elastic cable driven surgical robot using Unscented Kalman Filter

Cable driven power transmission is popular in many manipulator applications including medical arms. In spite of advantages obtained by removing motors from the mechanism, cable transmission introduces higher non-linearity and more uncertainties such as cable stretch and cable coupling. In order to improve the control precision and robustness of the Raven-II surgical robot, particularly for automation applications, the Unscented Kalman Filter (UKF) was adopted for state estimation. The UKF estimated state variables of the Raven-II dynamic model from sensor data. The dual UKF was used offline to estimate cable coupling parameters. The experimental results showed that the proposed method improved joint position estimation precision and the estimation consistency, especially on the more elastic links. The improvements for links 2 and 3 of the Raven were 36.76%, and 62.99%, respectively. For link 1 the improvement was 1.43% because the transmission is very stiff.

Hysteresis model of longitudinally loaded cable for cable driven robots and identification of the parameters

In this paper, we propose model of longitudinally loaded cable based on the Bouc-Wen hysteresis model and within the framework of the Duhem operator. By optimizing the 9 hysteresis model parameters with a genetic algorithm, the proposed model is shown to be capable of representing quasi-static response of two different diameter cables, 0.61 mm (thin) and 1.19 mm (thick), used for the RAVEN II surgical robotic surgery platform. The construction of the cable is 7 strands with 19 individual wires per strand. Furthermore, it is shown that the dynamic response of the cables are captured by adding a linear damping term. The hysteresis model and linear damper with the optimized parameters accurately models a longitudinal vibration test result in terms of frequency, steady state stretch, and logarithmic decrement. Energy dissipation due solely to the hysteresis term is approximately calculated to be 57 and 71% of the total energy loss for the thin and thick cables respectively. The proposed model may be used for cables with different contraction and diameter and can be applied for control of cable driven robots in which cables are stretched longitudinally without large excitation of other modes.

Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015

Purpose Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods In this work, we describe and present the results of the Head and Neck Auto‐Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results This paper presents the quantitative results of this challenge using multiple established error metrics and a well‐defined ranking system. The strengths and weaknesses of the different auto‐segmentation approaches are analyzed and discussed. Conclusions The Head and Neck Auto‐Segmentation Challenge 2015 was a good opportunity to assess the current state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms.

Gaussian Process Regression for Sensorless Grip Force Estimation of Cable-Driven Elongated Surgical Instruments

Haptic feedback is a critical but a clinically missing component in robotic minimally invasive surgeries. This paper proposes a Gaussian process regression (GPR) based scheme to address the gripping force estimation problem for clinically commonly used elongated cable-driven surgical instruments. Based on the cable-driven mechanism property studies, and surgical robotic system properties, four different GPR filters were designed and analyzed, including one GPR filter with two-dimensional inputs, one GPR filter with three-dimensional inputs, one GPR unscented Kalman filter (UKF) with two-dimensional inputs, and one GPR UKF with three-dimensional inputs. The four proposed methods were compared with the dynamic model based UKF filter on a 10 mm gripper on the Raven II surgical robot platform. The experimental results demonstrated that the four proposed methods outperformed the dynamic model based method on precision and reliability without parameter tuning. And surprisingly, among the four methods, the simplest GPR Filter with two-dimensional inputs has the best performance.

Unscented Kalman Filter and 3D vision to improve cable driven surgical robot joint angle estimation

Cable driven manipulators are popular in surgical robots due to compact design, low inertia, and remote actuation. In these manipulators, encoders are usually mounted on the motor, and joint angles are estimated based on transmission kinematics. However, due to non-linear properties of cables such as cable stretch, lower stiffness, and uncertainties in kinematic model parameters, the precision of joint angle estimation is limited with transmission kinematics approach. To improve the positioning of these manipulators, we use a pair of low cost stereo camera as the observation for joint angles and we input these noisy measurements into an Unscented Kalman Filter (UKF) for state estimation. We use the dual UKF to estimate cable parameters and states offline. We evaluated the effectiveness of the proposed method on a Raven-II experimental surgical research platform. Additional encoders at the joint output were employed as a reference system. From the experiments, the UKF improved the accuracy of joint angle estimation by 33- 72%. Also, we tested the reliability of state estimation under camera occlusion. We found that when the system dynamics is tuned with offline UKF parameter estimation, the camera occlusion has no effect on the online state estimation.

Dynamic modeling of cable driven elongated surgical instruments for sensorless grip force estimation

Haptic feedback plays a key role in surgeries, but it is still a missing component in robotic Minimally Invasive Surgeries. This paper proposes a dynamic model-based sensorless grip force estimation method to address the haptic perception problem for commonly used elongated cable-driven surgical instruments. Cable and cable-pulley properties are studied for dynamic modeling; grip forces, along with driven motor and gripper jaw positions and velocities are jointly estimated with Unscented Kalman Filter and only motor encoder readings and motor output torques are assumed to be known. A bounding filter is used to compensate for model inaccuracy and to improve method robustness. The proposed method was validated on a 10mm gripper which is driven by a Raven-II surgical robot. The gripper was equipped with 1-dimensional force sensors which served as ground truth data. The experimental results showed that the proposed method provides sufficiently good grip force estimation, while only motor encoder and the motor torques are used as observations.

An Automated Methodology for Assessing Anatomy-Specific Instrument Motion during Endoscopic Endonasal Skull Base Surgery

Objectives Describe instrument motion during live endoscopic skull base surgery (ESBS) and evaluate kinematics within anatomic regions. Design Case series. Setting Tertiary academic center. Participants A single skull base surgeon performed six anterior skull base approaches to the pituitary. Main Outcomes and Measures Time‐stamped instrument coordinates were recorded using an optical tracking system. Kinematics (i.e., mean cumulative instrument travel, velocity, acceleration, and angular velocity) was calculated by anatomic region including nasal vestibule, anterior and posterior ethmoid, sphenoid, and lateral opticocarotid recess (lOCR) regions. Results We observed mean (standard deviation, SD) velocities of 6.14 cm/s (1.55) in the nasal vestibule versus 1.65 cm/s (0.34) near the lOCR. Mean (SD) acceleration was 7,480 cm/s2 (5790) in the vestibule versus 928 cm/s2 (662) near the lOCR. Mean (SD) angular velocity was 17.2 degrees/s (8.31) in the vestibule and 5.37 degrees/s (1.09) near the lOCR. We observed a decreasing trend in the geometric mean velocity, acceleration, and angular velocity when approaching the pituitary (p < 0.001). Conclusion Using a novel method for analyzing instrument motion during live ESBS, we observed a decreasing trend in kinematics with proximity to the pituitary. Additional characterization of surgical instrument motion is paramount for optimizing patient safety and training.

Region-Specific Objective Signatures of Endoscopic Surgical Instrument Motion: A Cadaveric Exploratory Analysis

Objectives The objective of this study was to evaluate region‐specific surgical instrument kinematics among novice and experienced surgeons performing endoscopic endonasal skull base surgery. Design Cadaveric experimental study. Setting Tertiary academic center. Participants Two novice and two experienced surgeons performed eight endoscopic total ethmoidectomies and sphenoidotomies using an optically tracked microdebrider. Main Outcome Measures Time‐stamped Euclidian coordinates were recorded. Cumulative instrument travel, mean linear velocity and acceleration, and mean angular velocities were calculated in the anterior ethmoid, posterior ethmoid, and sphenoid sinus regions. Results Mean cumulative instrument travel (standard deviation) was highest in the posterior ethmoid region for both novice and experienced surgeons (9,795 mm [1,664] vs. 3,833 mm [1,080]). There was a trend in mean linear and angular velocities, and acceleration with increasing magnitudes for experienced surgeons compared with novices. Among experienced surgeons, we observed a trend of decreasing yaw velocity during the approach to the surgical target. Conclusions We present a novel method of evaluating surgical instrument motion with respect to anatomical regions of the skull base during endoscopic endonasal skull base surgery. These data may be used in the development of surgical monitoring and training systems to optimize patient safety.

Roboscope: A flexible and bendable surgical robot for single portal Minimally Invasive Surgery

Minimally Invasive Surgery (MIS) can reduce iatrogenic injury and decrease the possibility of surgical complications. This paper presents a novel flexible and bendable endoscopic device, “Roboscope”, which delivers two instruments, two miniature scanning fiber endoscopes, and a suction/irrigation port to the operation site through a single portal. Compared with existing bendable and steerable robotic surgical systems, Roboscope provides two bending degrees of freedom for its outer sheath and two insertion degrees of freedom, while simultaneously delivering two instruments and two endoscopes to the surgical site. Each bending axis and insertion freedom of Roboscope is independently controllable via an external actuation pack. Surgical tools can be changed without retracting the robot arm. This paper presents the design of the Roboscope mechanical system, electrical system, and control and software systems, design requirements and prototyping validation as well as analysis of Roboscope workspece.

Efficient orbital structures segmentation with prior anatomical knowledge

Abstract. We present a fully automatic method for segmenting orbital structures (globes, optic nerves, and extraocular muscles) in CT images. Prior anatomical knowledge, such as shape, intensity, and spatial relationships of organs and landmarks, were utilized to define a volume of interest (VOI) that contains the desired structures. Then, VOI was used for fast localization and successful segmentation of each structure using predefined rules. Testing our method with 30 publicly available datasets, the average Dice similarity coefficient for right and left sides of [0.81, 0.79] eye globes, [0.72, 0.79] optic nerves, and [0.73, 0.76] extraocular muscles were achieved. The proposed method is accurate, efficient, does not require training data, and its intuitive pipeline allows the user to modify or extend to other structures.