Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning
NUS Graduate School for Integrative Sciences and Engineering (NGS)（新加坡国立大学综合科学与工程研究生院（NGS））；National University of Singapore（新加坡国立大学）；University of Moratuwa（莫拉图瓦大学）；NIT Trichy（印度国立理工学院蒂鲁吉拉伯利分校）
Mobarakol Islam, Daniel A. Atputharuban, Ravikiran Ramesh, Hongliang Ren
Robot-assisted surgery is an emerging technology which has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provided contextual information about the different tissues and instruments being operated with. For this purpose, we have developed a light-weight cascaded convolutional neural network (CNN) to segment the surgical instruments from high-resolution videos obtained from a commercial robotic system. We propose a multi-resolution feature fusion module (MFF) to fuse the feature maps of different dimensions and channels from the auxiliary and main branch. We also introduce a novel way of combining auxiliary loss and adversarial loss to regularize the segmentation model. Auxiliary loss helps the model to learn low-resolution features, and adversarial loss improves the segmentation prediction by learning higher order structural information. The model also consists of a light-weight spatial pyramid pooling (SPP) unit to aggregate rich contextual information in the intermediate stage. We show that our model surpasses existing algorithms for pixel-wise segmentation of surgical instruments in both prediction accuracy and segmentation time of high-resolution videos.
Massachusetts Institute of Technology（麻省理工学院）；Neuromation OU；University of Michigan（密歇根大学）；Lyft Inc.（来福公司）
Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our winning solution for MICCAI 2017 Endoscopic Vision SubChallenge: Robotic Instrument Segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed…
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
The Chinese University of Hong Kong（香港中文大学）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）
hutom；Yonsei University College of Medicine（延世大学医学院）
Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery or surgical index after surgery to the surgeon. We introduce a new dataset that reflects the kinematic characteristics of surgical instruments for automated surgical instrument localization of surgical videos. The hSDB(hutom Surgery DataBase)-instrument dataset consists of instrument localization information from 24 cases of laparoscopic cholecystecomy and 24 cases of robotic gastrectomy. Localization information for…
Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Imperial College London（帝国理工学院）；Hong Kong University of Science and Technology（香港科技大学）；The General Hospital of Southern Theatre Command of PLA（中国人民解放军南部战区总医院）；National University of Singapore（新加坡国立大学）
Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)（Wellcome/EPSRC介入与外科科学中心（WEISS））；University College London（伦敦大学学院）；Dept. of Computer Science, University College London（伦敦大学学院计算机科学系）
Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery
National University of Singapore（新加坡国立大学）；NUSRI Suzhou（新加坡国立大学苏州研究院）；University College London（伦敦大学学院）；The Chinese University of Hong Kong（香港中文大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究院）
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）；The Chinese University of Hong Kong（香港中文大学）
本文在 EndoVis 2017/2018 上实证评估 SAM 的手术器械零样本分割、扰动鲁棒性与提示表现，并通过 LoRA 微调提出无需提示的 SurgicalSAM。
Rochester Institute of Technology（罗切斯特理工学院）；Center for Imaging Science（影像科学中心）；Biomedical Engineering（生物医学工程）
Conventional therapy approaches limit surgeons' dexterity control due to limited field-of-view. With the advent of robot-assisted surgery, there has been a paradigm shift in medical technology for minimally invasive surgery. However, it is very challenging to track the position of the surgical instruments in a surgical scene, and accurate detection & identification of surgical tools is paramount. Deep learning-based semantic segmentation in frames of surgery videos has the potential to facilitate this task. In this work, we modify the U-Net architecture named U-NetPlus, by introducing a pre-tr…
Digital Surgery Ltd, Medtronic（美敦力数字外科有限公司）；ICube, University of Strasbourg, CNRS, IHU Strasbourg（斯特拉斯堡大学ICube实验室、法国国家科学研究中心、斯特拉斯堡大学医院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Hutom（Hutom公司）；Xiamen University（厦门大学）；University of Electronic Science and Technology of China（电子科技大学）；National University of Singapore（新加坡国立大学）；National Institute of Technology, Raipur（赖布尔国家理工学院）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmen…
Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与培训中心）
We study the task of semantic segmentation of surgical instruments in robotic-assisted surgery scenes. We propose the Instance-based Surgical Instrument Segmentation Network (ISINet), a method that addresses this task from an instance-based segmentation perspective. Our method includes a temporal consistency module that takes into account the previously overlooked and inherent temporal information of the problem. We validate our approach on the existing benchmark for the task, the Endoscopic Vision 2017 Robotic Instrument Segmentation Dataset, and on the 2018 version of the dataset, whose anno…
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Chinese University of Hong Kong（香港中文大学）
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Department of Computer Science, University of Freiburg（弗赖堡大学计算机科学系）；Zuse School ELIZA（Zuse学校ELIZA）；NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec AI Institute（魁北克人工智能研究所）；Collaborative Research Institute Intelligent Oncology (CRIION)（智能肿瘤学协作研究机构）
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学综合科学与工程研究生院）；Dept. of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Dept. of Instrumentation and Control Engineering, NIT Trichy（印度国立理工学院蒂鲁奇拉帕利分校仪器与控制工程系）；Dept. of Otolaryngology, Singapore General Hospital（新加坡中央医院耳鼻喉科）
Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate the camera control enables the surgeon to concentrate more on dealing with surgical instruments. The objective is to reduce the operation time and facilitate the surgery for both surgeons and patients. We propose an end-to-end trainable Spatio-Temporal Multi-Task Learning (ST-MTL) model with a shared encoder and spatio-temporal decoders for the real-time surgical instrument segmentation and task-oriented saliency dete…
Tecnologico de Monterrey（蒙特雷科技大学）；University of Oxford（牛津大学）；Oxford NIHR Biomedical Research Centre（牛津国家卫生研究院生物医学研究中心）
Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 cha…
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for masking of augmented reality overlays during surgical procedures. Most applications rely on accurate real-time segmentation of high-resolution surgical images. While previous research focused primarily on methods that deliver high accuracy segmentation masks, majority of them can not be used for real-time applications due to their computational cost. In this work, we design a light-weight and highly-efficient deep residual…
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））；TU Dresden（德累斯顿工业大学）；Technische Universität Dresden（德累斯顿工业大学）
Estimating the remaining surgery duration (RSD) during surgical procedures can be useful for OR planning and anesthesia dose estimation. With the recent success of deep learning-based methods in computer vision, several neural network approaches have been proposed for fully automatic RSD prediction based solely on visual data from the endoscopic camera. We investigate whether RSD prediction can be improved using unsupervised temporal video segmentation as an auxiliary learning task. As opposed to previous work, which presented supervised surgical phase recognition as auxiliary task, we avoid t…
German Center for Neuro-degenerative Diseases（德国神经退行性疾病中心）；Technischer Universität Darmstadt（达姆施塔特工业大学）；University Düsseldorf（杜塞尔多夫大学）
Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image. Methods: i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers lo…
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Intuitive Surgical Inc.（直观医疗公司）
Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery. In the majority of cases, the first step is the automatic segmentation of surgical tools. Prior work has focused on binary segmentation, where the objective is to label every pixel in an image as tool or background. We improve upon previous work in two major ways. First, we leverage recent techniques such as deep residual learning and dilated convolutions to advance binary-segmentation performance. Second, we extend the approach to multi-class se…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University College London（伦敦大学学院）
Surgical instrument segmentation -- in general a pixel classification task -- is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address the above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-…
Asan Medical Center（峨山医疗中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chungnam National University School of Medicine（忠南大学医学院）
Some endoscopic surgeries require a surgeon to hold the endoscope with one hand and the surgical instruments with the other hand to perform the actual surgery with correct vision. Recent technical advances in deep learning as well as in robotics can introduce robotics to these endoscopic surgeries. This can have numerous advantages by freeing one hand of the surgeon, which will allow the surgeon to use both hands and to use more intricate and sophisticated techniques. Recently, deep learning with convolutional neural network achieves state-of-the-art results in computer vision. Therefore, the …
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module…
Inserm, UMR 1101（法国国家健康与医学研究院）；Univ Bretagne Occidentale（西布列塔尼大学）；INSTITUT TELECOM（法国电信学院；布雷斯特电信学院）；TELECOM Bretagne（布雷斯特大学医院）；CHRU Brest
The amount of surgical data, recorded during video-monitored surgeries, has extremely increased. This paper aims at improving existing solutions for the automated analysis of cataract surgeries in real time. Through the analysis of a video recording the operating table, it is possible to know which instruments exit or enter the operating table, and therefore which ones are likely being used by the surgeon. Combining these observations with observations from the microscope video should enhance the overall performance of the systems. To this end, the proposed solution is divided into two main pa…
University of Bern（伯尔尼大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University College London（伦敦大学学院）；Bern University Hospital（伯尔尼大学医院）
Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion…
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学NUS综合科学与工程研究生院（NGS））；National University of Singapore（新加坡国立大学）；University of Michigan-Joint Institute, Shanghai Jiao Tong University（上海交通大学密歇根联合学院）
Directing of the task-specific attention while tracking instrument in surgery holds great potential in robot-assisted intervention. For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction. Our model is designed with a weight-shared encoder and two task-oriented decoders and optimized for the joint tasks. We introduce batch-Wasserstein (bW) loss and construct a soft attention module to refine the distinctive visual region for efficient saliency learning. For multitask optimization, it is always c…
Surgical scene understanding and multi-tasking learning are crucial for image-guided robotic surgery. Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource. The perception drawn can be applied in effective real-time feedback, surgical skill assessment, and human-robot collaborative surgeries to enhance surgical outcomes. For this purpose, we develop a novel end-to-end trainable real-time Multi-Task Learning (MTL) model with weight-shared encoder and task-aware detection and segment…
Robotic-assisted surgeries benefit both surgeons and patients, however, surgeons frequently need to adjust the endoscopic camera to achieve good viewpoints. Simultaneously controlling the camera and the surgical instruments is impossible, and consequentially, these camera adjustments repeatedly interrupt the surgery. Autonomous camera control could help overcome this challenge, but most existing systems are reactive, e.g., by having the camera follow the surgical instruments. We propose a predictive approach for anticipating when camera movements will occur using artificial neural networks. We…
The Chinese University of Hong Kong（香港中文大学）；Imperial College London（帝国理工学院）
Automatic instrument segmentation in video is an essentially fundamental yet challenging problem for robot-assisted minimally invasive surgery. In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation. Our inferred prior can provide reliable indication of the instrument location and shape, which is propagated from the previous frame to the current frame according to inter-frame motion flow. This prior is injected to the middle of an encoder-decoder segmentation netw…
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））；Intuitive Surgical（直觉外科公司）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；CHU Grenoble Alpes（格勒诺布尔阿尔卑斯大学医院）；Grenoble INP（格勒诺布尔综合理工学院）；TIMC-IMAG（TIMC-IMAG实验室）；Centre for Medical Image Computing（医学影像计算中心）；University College London（伦敦大学学院）；Department of General, Visceral and Transplant Surgery, University of Heidelberg（海德堡大学普通、内脏和移植外科系）；ARTORG Center, University of Bern（伯尔尼大学ARTORG中心）；Computer Aided Medical Procedures, Johns Hopkins University（约翰霍普金斯大学计算机辅助医疗程序）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院（KIT）人机工程与机器人研究所）
Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware like tracking systems or the robot encoders are cumbersome and lack accuracy, surgical vision is evolving as promising techniques to segment and track the instruments using only the endoscopic images. However, what is missing so far are common image data sets for consistent evaluation and benchmarking of algorithms against each other. The paper presents a comparative validation study of different vision-based methods for instrument s…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Heidelberg（海德堡大学）
During laparoscopic surgery, context-aware assistance systems aim to alleviate some of the difficulties the surgeon faces. To ensure that the right information is provided at the right time, the current phase of the intervention has to be known. Real-time locating and classification the surgical tools currently in use are key components of both an activity-based phase recognition and assistance generation. In this paper, we present an image-based approach that detects and classifies tools during laparoscopic interventions in real-time. First, potential instrument bounding boxes are detected us…
National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国学院）；Singapore General Hospital（新加坡中央医院）
Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery. For this purpose, we develop an approach to generate the scene graph and predict surgical interactions between instruments and surgical region of interest (ROI) during robot-assisted surgery. We design an attention link function and integrate with a graph parsing network to recognize the surgical interactions. To embed each node with corresponding neighbouring node features, we further incorporate SageConv into the …
Video Dataset for Surgical Phase, Keypoint, and Instrument Recognition in Laparoscopic Surgery (PhaKIR)
Regensburg Medical Image Computing (ReMIC), OTH Regensburg（雷根斯堡医学影像计算中心（ReMIC））；Regensburg Center of Biomedical Engineering (RCBE), OTH Regensburg（雷根斯堡生物医学工程中心（RCBE））；Regensburg University（雷根斯堡大学）；Regensburg Center of Health Sciences（雷根斯堡健康科学中心）；Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital（慕尼黑技术大学医院）；Biomedical Image Analysis Group, Department of Computing, Imperial College London（生物医学影像分析组，计算部门，伦敦帝国学院）；Research Group MITI, TUM University Hospital, School of Medicine（MITI研究组，慕尼黑技术大学医院，医学学院）；Department of Surgery, TUM University Hospital, School of Medicine（外科部门，慕尼黑技术大学医院，医学学院）
Instrument-tissue Interaction Detection Framework for Surgical Video Understanding
National University of Singapore（新加坡国立大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Agency for Science, Technology, and Research（新加坡科技研究局）；Osaka University Graduate School of Medicine（大阪大学医学部）
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
University College London（伦敦大学学院）；National Hospital for Neurology and Neurosurgery（国立神经学与神经外科医院）；Centre for AI and Robotics (CAIR) HKISI, CAS（中国科学院香港创新研究院人工智能与机器人中心）；Shanghai Jiao Tong University（上海交通大学）
Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view. While many recent approaches based on convolutional neural networks have shown great results, a key barrier to progress lies in the acquisition of a large number of manually-annotated images which is necessary for an algorithm to generalize and work well in diverse surgical scenarios. Unlike the surgical image data itself, annotations are difficult to acquire and may be o…
SimulaMet（西穆拉大都会实验室）；UiT The Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；University of Oxford（牛津大学）；NIHR Oxford Biomedical Research Centre（英国国家健康研究所牛津生物医学研究中心）
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have demonstrated potential for computer-assisted procedures. However, there exists challenges and requirements to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can be…
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences（Wellcome/EPSRC介入与外科科学中心）；University College London（伦敦大学学院）；Xiamen University（厦门大学）；The University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）
Intra-operative anticipation of instrument usage is a necessary component for context-aware assistance in surgery, e.g. for instrument preparation or semi-automation of robotic tasks. However, the sparsity of instrument occurrences in long videos poses a challenge. Current approaches are limited as they assume knowledge on the timing of future actions or require dense temporal segmentations during training and inference. We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations. During training, only sparse instrument annotati…
Imperial College London（伦敦帝国理工学院）；Tsinghua University（清华大学）；NYU Abu Dhabi（纽约大学阿布扎比分校）；Wuhan University（武汉大学）；University College London（伦敦大学学院）；University of Liverpool（利物浦大学）
Autonomous suturing has been a long-sought-after goal for surgical robotics. Outside of staged environments, accurate localization of suture needles is a critical foundation for automating various suture needle manipulation tasks in the real world. When localizing a needle held by a gripper, previous work usually tracks them separately without considering their relationship. Because of the significant errors that can arise in the stereo-triangulation of objects and instruments, their reconstructions may often not be consistent. This can lead to unrealistic tool-needle grasp reconstructions tha…
SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery
Robotics and Control Laboratory, University of British Columbia（机器人与控制实验室，不列颠哥伦比亚大学）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；Division of Otolaryngology – Head and Neck Surgery, University of British Columbia（耳鼻喉科–头颈外科部，不列颠哥伦比亚大学）
