Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
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）
Sebastian Bodenstedt, Max Allan, Anthony Agustinos, Xiaofei Du, Luis Garcia-Peraza-Herrera, Hannes Kenngott, Thomas Kurmann, Beat Müller-Stich, Sebastien Oursel… 展开作者
Sebastian Bodenstedt, Max Allan, Anthony Agustinos, Xiaofei Du, Luis Garcia-Peraza-Herrera, Hannes Kenngott, Thomas Kurmann, Beat Müller-Stich, Sebastien Ourselin, Daniil Pakhomov, Raphael Sznitman, Marvin Teichmann, Martin Thoma, Tom Vercauteren, Sandrine Voros, Martin Wagner, Pamela Wochner, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel
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 segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery. The contribution of the paper is twofold: we introduce a comprehensive validation data set that was provided to the study participants and present the results of the comparative validation study. Based on the results of the validation study, we arrive at the conclusion that modern deep learning approaches outperform other methods in instrument segmentation tasks, but the results are still not perfect. Furthermore, we show that merging results from different methods actually significantly increases accuracy in comparison to the best stand-alone method. On the other hand, the results of the instrument tracking task show that this is still an open challenge, especially during challenging scenarios in conventional laparoscopic surgery.
IRCAD (Institut de Recherche contre les Cancers de l’Appareil Digestif)（IRCAD（消化系统癌症研究所））；ICube (UMR 7357 CNRS), Université de Strasbourg（ICube（UMR 7357 CNRS），斯特拉斯堡大学）；IHU, Institut Hospitalo-Universitaire（IHU，大学医院研究所）；Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza（阿拉贡工程研究所（I3A），萨拉戈萨大学）
Recovering surgical scene structure in laparoscope surgery is crucial step for surgical guidance and augmented reality applications. In this paper, a quasi dense reconstruction algorithm of surgical scene is proposed. This is based on a state-of-the-art SLAM system, and is exploiting the initial exploration phase that is typically performed by the surgeon at the beginning of the surgery. We show how to convert the sparse SLAM map to a quasi dense scene reconstruction, using pairs of keyframe images and correlation-based featureless patch matching. We have validated the approach with a live por…
Centre for Autonomous Systems, University of Technology, Sydney（悉尼科技大学自主系统中心）；University of Technology, Sydney（悉尼科技大学）
Real-time simultaneously localization and dense mapping is very helpful for providing Virtual Reality and Augmented Reality for surgeons or even surgical robots. In this paper, we propose MIS-SLAM: a complete real-time large scale dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB- SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in previous work, that is, fast movement of scope and blurry …
University of Hamburg（汉堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical instrument segmentation challenging. To cope with these challenges, we adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask…
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…
Bournemouth University（伯恩茅斯大学）；University of Chester（切斯特大学）
The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-time and accurate augmented information overlay in MIS is still a formidable task. In this paper, we present a novel real-time AR framework for MIS that achieves interactive geometric aware augmented reality in endoscopic surgery with stereo views. Our framework tracks the movement of the endoscopic camera and simultaneously reconstru…
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…
Minimally invasive image guided treatment procedures often employ advanced image processing algorithms. The recent developments of artificial intelligence algorithms harbor potential to further enhance this domain. In this article we explore several application areas within the minimally invasive treatment space and discuss the deployment of artificial intelligence within these areas.
Bournemouth University（伯恩茅斯大学）；University of Chester（切斯特大学）；University of Bradford（布拉德福德大学）
One of the major challenges in Minimally Invasive Surgery (MIS) such as laparoscopy is the lack of depth perception. In recent years, laparoscopic scene tracking and surface reconstruction has been a focus of investigation to provide rich additional information to aid the surgical process and compensate for the depth perception issue. However, robust 3D surface reconstruction and augmented reality with depth perception on the reconstructed scene are yet to be reported. This paper presents our work in this area. First, we adopt a state-of-the-art visual simultaneous localization and mapping (SL…
SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery
University of Michigan（密歇根大学）；University of Edinburgh（爱丁堡大学）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；University of Colorado（科罗拉多大学）；University of Rochester（罗切斯特大学）；Michigan Medicine（密歇根医学）
University of Michigan（密歇根大学）；United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；Imperial College London（伦敦帝国理工学院）
This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed method is built on the fast ''dense inverse searching'' algorithm, which estimates the disparity of the stereo images. The overlapping image patches (arbitrary squared image segment) from the images at different scales are aligned based on the photometric consistency presumption. We propose a Bayesian framework to evaluate the probability of the optimized patch disparity at different scales. Moreover, we introduce a spatial Gaussian mixed pr…
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；PAII Inc（PAII公司）
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-…
Expert Consensus-based Video-Based Assessment Tool for Workflow Analysis in Minimally Invasive Colorectal Surgery: Development and Validation of ColoWorkflow
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France（斯特拉斯堡大学，CNRS，INSERM，ICube，UMR7357，法国斯特拉斯堡）；Department of Medical-Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Italy（医学与外科科学与转化医学系，医学与心理学学院，罗马萨皮恩扎大学，意大利）；University Hospital Virgen Macarena, University of Sevilla, Seville, Spain（圣地亚哥医院，塞维利亚大学，西班牙塞维利亚）；Department of General and Minimally Invasive Surgery, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy（普通外科与微创外科部门，IRCCS卡格拉奥斯皮奇亚诺医院，米兰，意大利）；Arizona State University, Tempe, AZ, USA（亚利桑那州立大学，Tempe，美国亚利桑那）
Suture needle localization is necessary for autonomous suturing. Previous approaches in autonomous suturing often relied on fiducial markers rather than markerless detection schemes for localizing a suture needle due to the inconsistency of markerless detections. However, fiducial markers are not practical for real-world applications and can often be occluded from environmental factors in surgery (e.g., blood). Therefore in this work, we present a robust tracking approach for estimating the 6D pose of a suture needle when using inconsistent detections. We define observation models based on sut…
Multitask Learning in Minimally Invasive Surgical Vision: A Review
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学与技术研究院）
University of Technology, Sydney（悉尼科技大学）；Centre for Autonomous Systems（自主系统中心）
In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This paper proposes an innovative Simultaneous Localization and Mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the Embedded Deformation (ED) nodes with 3D shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can: (1) Increment…
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）
Leibniz Universität Hannover（莱布尼茨汉诺威大学）；Hamburg University of Technology（汉堡工业大学）
Purpose: The facial recess is a delicate structure that must be protected in minimally invasive cochlear implant surgery. Current research estimates the drill trajectory by using endoscopy of the unique mastoid patterns. However, missing depth information limits available features for a registration to preoperative CT data. Therefore, this paper evaluates OCT for enhanced imaging of drill holes in mastoid bone and compares OCT data to original endoscopic images. Methods: A catheter-based OCT probe is inserted into a drill trajectory of a mastoid phantom in a translation-rotation manner to acqu…
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…
This work proves that semantic segmentation on minimally invasive surgical instruments can be improved by using training data that has been augmented through domain adaptation. The benefit of this method is twofold. Firstly, it suppresses the need of manually labeling thousands of images by transforming synthetic data into realistic-looking data. To achieve this, a CycleGAN model is used, which transforms a source dataset to approximate the domain distribution of a target dataset. Secondly, this newly generated data with perfect labels is utilized to train a semantic segmentation neural networ…
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…
City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong（香港中文大学）；Soochow University（苏州大学）
Objective: The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Methods: Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of complex anatomical structures is obtained. To reconstruct the 3D structure of the whole surgical environmen…
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）
SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
Intuitive Surgical, Inc.（Intuitive Surgical公司）；Johns Hopkins Medicine Neurosurgery（约翰霍普金斯医学神经外科）；Johns Hopkins University Electrical and Computer Engineering（约翰霍普金斯大学电气与计算机工程）；University of British Columbia Electrical and Computer Engineering（不列颠哥伦比亚大学电气与计算机工程）；Wilford & Kate Bailey Small Animal Teaching Hospital（威尔福德与凯蒂·贝利小动物教学医院）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle Hospital（卡尔医院）；Carle Illinois College of Medicine（卡尔伊利诺伊医学院）
We present a novel thermodynamic parameter estimation framework for energy-based surgery on live tissue, with direct applications to tissue characterization during electrosurgery. This framework addresses the problem of estimating tissue-specific thermodynamics in real-time, which would enable accurate prediction of thermal damage impact to the tissue and damage-conscious planning of electrosurgical procedures. Our approach provides basic thermodynamic information such as thermal diffusivity, and also allows for obtaining the thermal relaxation time and a model of the heat source, yielding in …
The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a sit…
The Hamlyn Centre for Robotic Surgery, IGHI, Imperial College London（伦敦帝国理工学院哈姆林机器人手术中心）；Dyson Robotics Laboratory, Imperial College London（伦敦帝国理工学院戴森机器人实验室）；OpenAI；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与癌症系）
Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as …
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…
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗保健有限公司）；Hochschule Furtwangen（富特旺根应用技术大学）；BG Trauma Center Ludwigshafen（路德维希港BG创伤中心）
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent ove…
SLAM assisted 3D tracking system for laparoscopic surgery
United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；University of Michigan（密歇根大学）；United Imaging（联影医疗）；United Imaging of Surgery（联影外科研究院）
Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light
School of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）；MicroPort MedBot (Group) Company Ltd.（微创医疗机器人（集团）股份有限公司）；College of Engineering, Peking University（北京大学工学院）；City University of Hong Kong（香港城市大学）
End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery
Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系，瑞士Allschwil）；Department of Orthopedics, University Children’s Hospital, Basel, Switzerland（巴塞尔大学儿童医院骨科部，瑞士Basel）
The Chinese University of Hong Kong（香港中文大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally invasive surgery, rich information including surgical videos and robotic kinematics can be recorded, which provide complementary knowledge for understanding surgical gestures. However, existing methods either solely adopt uni-modal data or directly concatenate multi-modal representations, which can not sufficiently exploit the informative correlations inherent in visual and kinematics data to boost gesture recogniti…
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）
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 be…
EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery
Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；School of Advanced Manufacturing, Fuzhou University（福州大学先进制造学院）
California Institute of Technology（加州理工学院）；University of Southern California（南加州大学）
Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown for the majority of surgical procedures, we hypothesize that the core elements of surgery must first be quantified in a reliable, objective, and scalable manner. We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pur…
Conventional computer-assisted orthopaedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. Visual tracking has recently been proposed to measure the target anatomy in a markerless and effortless way, but the existing methods fail under real-world occlusion caused by intraoperative interventions. Furthermore, such methods are hardware-specific and not accurate enough for surgical applications. In this paper, we propose a RGB-D sensing-based markerless tracking method that is robust a…
