2021 International Symposium on Medical Robotics (ISMR)最新文献_第2页

A Unified Control Framework with Continuous Speed Adaptation used for Powered Prostheses Control 基于连续速度自适应的动力假肢统一控制框架

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661548

P. Sherpa, David Quintero

{"title":"A Unified Control Framework with Continuous Speed Adaptation used for Powered Prostheses Control","authors":"P. Sherpa, David Quintero","doi":"10.1109/ismr48346.2021.9661548","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661548","url":null,"abstract":"A challenge for lower-limb powered prostheses is developing a seamless control strategy to perform multiple locomotion tasks, such as changes in walking speed. Generally, powered prostheses implement different independent controllers that correspond to a specific task that each contain their own patient-specific control parameters to tune. This paper presents an online parameterize method of providing desired joint kinematic trajectories for a powered knee-ankle prosthesis controller to perform continuously smooth kinematic transitions unified across the gait cycle for level-ground activity. An active Catmull-Rom spline model generates the online desired knee and ankle joint trajectories as a virtual constraint controller that is a function of a phase variable and human desired speed. An offline optimization routine was implemented to produce optimal control point locations for the Catmull-Rom spline model to give transit across different kinematic walking speeds in a continuous manner. Results demonstrate speed adaptation for different walking speeds (i.e., slow, normal, and fast) as well as running to show versatility towards an adaptive unified virtual constraint control.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132303846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The SEA-Scope: Torque-limited endoscopic joint control for telemanipulation or visual servoing through tendon force control with series elastic actuation SEA-Scope:通过串联弹性驱动的肌腱力控制，用于遥控或视觉伺服的扭矩有限的内窥镜关节控制

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661497

Lorin Fasel, N. Gerig, P. Cattin, G. Rauter

{"title":"The SEA-Scope: Torque-limited endoscopic joint control for telemanipulation or visual servoing through tendon force control with series elastic actuation","authors":"Lorin Fasel, N. Gerig, P. Cattin, G. Rauter","doi":"10.1109/ismr48346.2021.9661497","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661497","url":null,"abstract":"When performing minimally invasive surgeries, surgeons are currently restricted by the rigidity and limited maneuverability of their tools. The tools could be extended by joints to provide additional degrees of freedom. However, manually controlling the movement of distal joints is challenging since the effective interaction forces at the tip are difficult to feel. Therefore, manipulation of distal joints to increase the maneuverability can lead to additional risks for harming the patient. To overcome limited maneuverability while providing inherent safety, we propose a novel concept for minimally invasive tool actuation based on the principle of series elastic actuation. In previous work, we showed successful torque control of an articulated robotic endoscope. In this paper, we extended torque control by high-level position control. We evaluated the position control experimentally for the case of a telemanipulated joint as well as for automatic target following. Automatic target following was achieved with visual servoing, i.e., an image stream from a miniature camera was processed to compute the joint position setpoint. The results showed that accurate and stable position control is feasible with an actuation based on series elastic actuation. Compared to traditional robotic endoscope actuation, which is designed to be as stiff as possible, our approach reduced impact forces and allowed to set the torque limit in the joint as desired. Therefore, torques exerted by the endoscope joint to adjacent structures can be kept within desired limits.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"677 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127737376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced U-Net Tool Segmentation using Hybrid Coordinate Representations of Endoscopic Images 使用混合坐标表示内镜图像的增强U-Net工具分割

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661519

Kevin Huang, Digesh Chitrakar, Wenfan Jiang, Yun-Hsuan Su

{"title":"Enhanced U-Net Tool Segmentation using Hybrid Coordinate Representations of Endoscopic Images","authors":"Kevin Huang, Digesh Chitrakar, Wenfan Jiang, Yun-Hsuan Su","doi":"10.1109/ismr48346.2021.9661519","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661519","url":null,"abstract":"This paper presents an approach to enhanced endoscopic tool segmentation combining separate pathways utilizing input images in two different coordinate representations. The proposed method examines U-Net convolutional neural networks with input endoscopic images represented via (1) the original rectangular coordinate format alongside (2) a morphological polar coordinate transformation. To maximize information and the breadth of the endoscope frustrum, imaging sensors are oftentimes larger than the image circle. This results in unused border regions. Ideally, the region of interest is proximal to the image center. The above two observations formed the basis for the morphological polar transformation pathway as an augmentation to typical rectangular input image representations. Results indicate that neither of the two investigated coordinate representations consistently yielded better segmentation performance as compared to the other. Improved segmentation can be achieved with a hybrid approach that carefully selects which of the two pathways to be used for individual input images. Towards that end, two binary classifiers were trained to identify, given an input endoscopic image, which of the two coordinate representation segmentation pathways (rectangular or polar), would result in better segmentation performance. Results are promising and suggest marked improvements using a hybrid pathway selection approach compared to either alone. The experiment used to evaluate the proposed hybrid method utilized a dataset consisting of 8360 endoscopic images from real surgery and evaluated segmentation performance with Dice coefficient and Intersection over Union. The results suggest that on-the-fly polar transformation for tool segmentation is useful when paired with the proposed hybrid tool-segmentation approach.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129045319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Framework for Fast Automatic Robot Ultrasound Calibration 一种快速自动机器人超声标定框架

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661495

Ruixuan Li, K. Niu, E. V. Poorten

{"title":"A Framework for Fast Automatic Robot Ultrasound Calibration","authors":"Ruixuan Li, K. Niu, E. V. Poorten","doi":"10.1109/ismr48346.2021.9661495","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661495","url":null,"abstract":"Ultrasound (US) has been increasingly used as medical imaging technology across various clinical diagnostic and therapeutic scenarios thanks to its availability and non-radiative nature. While 3D US probes are becoming available, most systems are still using 2D probes. For 3D US reconstruction based on 2D probes, US image calibration forms an essential step. Through calibration, one can find the transformation matrix between a coordinate frame attached to an optical marker or the robot’s end effector towards the coordinate frame of the US probe. Current US calibration methods usually require hereto lengthy free hand gestures as well as some manual interventions, which hampers the use and integration with advanced robotic systems. This paper introduces a reliable automatic calibration framework that is also fast. Demonstrated on a KUKA lightweight robot and 2D US probe, the full calibration procedure was completed in 224.8 seconds with a 1.29 mm mean 3D localization error. Within this procedure, camera-to-robot calibration was accomplished within only 47 seconds and reached a 0.17 mm mean error. Validation of the US image calibration was done through 3D printed model, leading to a mean deviation of 1.05 mm from the respective CAD models.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127850967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Force Estimation on Steerable Catheters through Learning-from-Simulation with ex-vivo Validation* 基于模拟学习和离体验证的可操纵导尿管力估计*

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661549

A. Sayadi, Hamid Reza Nourani, M. Jolaei, J. Dargahi, Amir Hooshiar

{"title":"Force Estimation on Steerable Catheters through Learning-from-Simulation with ex-vivo Validation*","authors":"A. Sayadi, Hamid Reza Nourani, M. Jolaei, J. Dargahi, Amir Hooshiar","doi":"10.1109/ismr48346.2021.9661549","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661549","url":null,"abstract":"Monitoring and control of the contact force at the tip of soft flexural robots is of high application need, e.g., the tip force on radiofrequency ablation (RFA) catheters. In this study, a real-time tip force estimation method based on image-based shape-sensing and learning-from-simulation is provided. To this end, a generalized image-based shape-sensing technique for flexural robots was developed using the Bezier spline interpolation method. Afterward, the deflection of a commercial catheter subjected to a series of tip forces was simulated using nonlinear finite element modeling. Next, two independent data-driven models, i.e., artificial neural network (ANN) and support vector regression (SVR), were trained with a dataset with the Bezier spline control points as the inputs and tip forces as the output. For validation, the trained models were used for real-time tip force estimation while the catheter was pressed against porcine atrial tissue. The test was performed using a universal testing machine that recorded the ground-truth contact force. The comparison showed that the ANN model had a mean-absolute-error of 0.0217±0.0191 N, while the SVR model exhibited a mean absolute error of 0.0178 ± 0.0121 N and a correlation coefficient of 0.991. Moreover, the proposed method showed a minimum computational refresh rate of 646 Hz (ANN) and 917 Hz (SVR) during the validation experiment. The performance of the proposed method was in compliance with the clinical requirements of RFA therapy.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Continuous Prediction of Leg Kinematics During Ambulation using Peripheral Sensing of Muscle Activity and Morphology 利用肌肉活动和形态的外周感知连续预测行走过程中的腿部运动学

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661485

Kaitlin G. Rabe, Nicholas P. Fey

{"title":"Continuous Prediction of Leg Kinematics During Ambulation using Peripheral Sensing of Muscle Activity and Morphology","authors":"Kaitlin G. Rabe, Nicholas P. Fey","doi":"10.1109/ismr48346.2021.9661485","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661485","url":null,"abstract":"The advancement of robotic lower-limb assistive devices has heightened the need for accurate and continuous sensing of user intent. Surface electromyography (EMG) has been extensively used to sense muscles, and estimate locomotion modes and limb motion. Recently, sonomyography has also been investigated as a novel sensing modality. However, the fusion of multiple sensing modalities has not been explored for the continuous prediction of multiple degrees-of-freedom of the lower limb, and during multiple ambulation tasks. In the present study, nine able-bodied subjects completed level, incline, decline, stair ascent, and stair descent tasks. Motion capture data was collected during each task, as well as data from a portable ultrasound transducer (aligned in a transverse orientation) on the anterior thigh and surface EMG sensors on eight lower-limb muscles. Subject-dependent, task-independent Gaussian process regression models were implemented for continuous prediction of knee and ankle angle and angular velocity during these ambulation tasks using three feature sets: (1) surface EMG, (2) sonomyography, and (3) sensor fusion of EMG with sonomyography. Surprisingly, there were no significant differences between sonomyography and sensor fusion-based prediction of knee or ankle angle and angular velocity during all tasks. However, sonomyography and sensor fusion resulted in reduced root mean square error of knee angle prediction during all ambulation tasks and knee angular velocity prediction during most ambulation tasks compared to surface EMG. Sensor fusion improved ankle angle prediction for all walking tasks except stair ascent in comparison to surface EMG. Ankle angular velocity prediction resulted in the lowest performance, overall.Clinical Relevance—This work compares the combination of surface electromyography and sonomyography, and each modality in isolation, for the continuous prediction of kinematics of the knee and ankle during widely-varying ambulatory tasks.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125255912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Design of a 6 DoF Parallel Robot for MRI-guided Interventions 六自由度mri引导干预并联机器人的设计

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661513

Mishek Musa, Saikat Sengupta, Yue Chen

引用次数: 4

Lymph Node Detection Using Robot Assisted Electrical Impedance Scanning and an Artificial Neural Network 利用机器人辅助电阻抗扫描和人工神经网络进行淋巴结检测

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661502

Alex Tinggaard Årsvold, Andreas Sørensen Zeltner, Zhuoqi Cheng, K. Schwaner, Pernille Tine Jensen, T. Savarimuthu

引用次数: 2

Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions 基于深度学习的模型-图像配准，用于图像引导的血管内介入

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661511

Zhen Li, M. Mancini, G. Monizzi, D. Andreini, G. Ferrigno, J. Dankelman, E. Momi

{"title":"Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions","authors":"Zhen Li, M. Mancini, G. Monizzi, D. Andreini, G. Ferrigno, J. Dankelman, E. Momi","doi":"10.1109/ismr48346.2021.9661511","DOIUrl":"https://doi.org/10.1109/ismr48346.2021.9661511","url":null,"abstract":"Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach taking advantage of the reuse of pre-operative 3D models reconstructed from Computed Tomography Angiography (CTA) images. Traditional optimized-based registration methods suffer severely from high computational complexity. Moreover, the consequence of lacking ground truth for learning-based registration approaches should not be neglected. To overcome these challenges, we introduce a model-to-image registration framework via deep learning for image-guided endovascular catheterization. This work performs autonomous vessel segmentation from intra-operative fluoroscopy images via a deep residual U-net and a model-to-image matching via a convolutional neural network. For this study, image data were collected from 10 patients who performed Transcatheter Aortic Valve Implantation (TAVI) procedures. It was found that vessel segmentation of test data results in median values of Dice Similarity Coefficient, Precision, and Recall of (0.75, 0.58, 0.67) for femoral artery, and (0.71, 0.56, 0.74) for aortic root. The segmentation network behaves better than manual annotation, and it recognizes part of vessels that were not labeled manually. Image matching between the transformed moving image and the fixed image results in a median value of Recall of 0.90. The proposed approach achieves a good accuracy of vessel segmentation and a good recall value of model-to-image matching.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":"12 Suppl 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126053316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Compliant Robotic Wrist Orthosis Driven by Twisted String Actuators 一种由扭弦驱动器驱动的柔性机器人腕部矫形器

2021 International Symposium on Medical Robotics (ISMR) Pub Date : 2021-11-17 DOI: 10.1109/ismr48346.2021.9661492

Thulani Tsabedze, Erik Hartman, Cianan Brennan, Jun Zhang

引用次数: 3