Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems. Such systems can potentially enhance efficiency in the OR, resulting in lower costs and improved care delivery to the patients. In this paper, we investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multi-perspective videos from a variety of robotic surgery cases captured using Time-of-Flight cameras. We densely annotate the videos with 10 most recognized and clinically relevant classes of activities. Furthermore, we investigate state-of-the-art computer vision action recognition techniques and adapt them for the OR environment and the dataset. First, we fine-tune the Inflated 3D ConvNet (I3D) for clip-level activity recognition on our dataset and use it to extract features from the videos. These features are then fed to a stack of 3 Temporal Gaussian Mixture layers which extracts context from neighboring clips, and eventually go through a Long Short Term Memory network to learn the order of activities in full-length videos. We extensively assess the model and reach a peak performance of 88% mean Average Precision.
University of Zagreb（萨格勒布大学）；University of Twente（特文特大学）；University of Groningen（格罗宁根大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Canisius-Wilhelmina Hospital（卡尼修斯-威廉明娜医院）
MIT（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Olympus Corporation（奥林巴斯公司）
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adversarial Network (GAN) formulation to sample future surgical phases trajectories conditioned on past video frames from laparoscopic cholecystectomy (LC) videos and compare it to state-of-the-art appro…
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 Hyogo（兵库大学）；Takatsuki Hospital（高槻医院）；International University of Business Agriculture & Technology（国际商业农业与技术大学）
Total knee arthroplasty (TKA) is a commonly performed surgical procedure to mitigate knee pain and improve functions for people with knee arthritis. The procedure is complicated due to the different surgical tools used in the stages of surgery. The recognition of surgical tools in real-time can be a solution to simplify surgical procedures for the surgeon. Also, the presence and movement of tools in surgery are crucial information for the recognition of the operational phase and to identify the surgical workflow. Therefore, this research proposes the development of a real-time system for the r…
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）
VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons
Centre for Artificial Intelligence and Robotics (CAIR), HKISI-CAS（香港科学院人工智能与机器人中心（CAIR））；The Chinese University of Hong Kong（香港中文大学）；King’s College London（伦敦国王学院）
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…
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）
Large-scale Self-supervised Video Foundation Model for Intelligent Surgery
The Hong Kong University of Science and Technology（香港科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；The Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；Tongji University（同济大学）；The Chinese University of Hong Kong（香港中文大学）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Shenzhen-Hong Kong Collaborative Innovation Research Institute（深港协同创新研究院）
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
Monash University（莫纳什大学）；Airdoc-Monash Research（Airdoc-莫纳什研究所）；Airdoc；Bosch Corporate Research（博世企业研究院）；Cornell University（康奈尔大学）；King’s College London（伦敦国王学院）；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属））；City University of Macau（澳门城市大学）；Eye Hospital, Wenzhou Medical University（温州医科大学附属眼视光医院）
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）
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）
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
The Chinese University of Hong Kong（香港中文大学）；Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.（华为技术有限公司中央研究院理论实验室2012实验室）；Chair of Computer Aided Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助程序教席）；Department of Gastroenterology, Qilu Hospital of Shandong University（山东大学齐鲁医院消化内科）
Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Department of Thoracic Surgery, the Seventh Affiliated Hospital, Sun Yat-sen University（中山大学第七附属医院胸外科部门）；Bioengineering/Imperial-X, Imperial College London（生物工程/Imperial-X，帝国理工学院伦敦分校）；ROAS Thrust, Hong Kong University of Science and Technology (Guangzhou)（ROAS项目，香港科技大学（广州））；Department of Electronic and Computer Engineering, Hong Kong SAR（香港特别行政区电子与计算机工程系）
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
The Chinese University of Hong Kong（香港中文大学）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院惠康/工程与物理科学研究委员会介入与外科科学中心）；National University of Singapore（新加坡国立大学）
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…
Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the potential to improve care delivery to patients and decrease costs. Recent works have achieved a promising performance on surgical activity recognition; however, the lack of generalizability of these models is one of the critical barriers to the wide-scale adoption of this technology. In this work, we study the generalizability of surgical activity recognition models across operating rooms. We propose a new domain adaptati…
INSTITUT MINES-TELECOM（法国国立高等矿业电信学校）；TELECOM Bretagne（布列塔尼电信学院）；UEB（布列塔尼西部大学）；LaTIM - INSERM UMR 1101（LaTIM - 法国国家健康与医学研究院 UMR 1101）；Univ Bretagne Occidentale（布列塔尼西大学）；CHRU Brest（布雷斯特大学医院）
The automatic analysis of the surgical process, from videos recorded during surgeries, could be very useful to surgeons, both for training and for acquiring new techniques. The training process could be optimized by automatically providing some targeted recommendations or warnings, similar to the expert surgeon's guidance. In this paper, we propose to reuse videos recorded and stored during cataract surgeries to perform the analysis. The proposed system allows to automatically recognize, in real time, what the surgeon is doing: what surgical phase or, more precisely, what surgical step he or s…
Autonomy is the frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this paper, we propose a novel calibration technique for a surgical scenario with da Vinci robot. Calibration of the camera and the robot is necessary for precise positioning of the tools in order to emulate the high performance surgeons. Our calibration technique is tailored for RGB-D camera. Different tests performed on relevant use cases for surgery prove …
Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes…
University College London (UCL)（伦敦大学学院）；King’s College London (KCL)（伦敦国王学院）；Moorfields Eye Hospital（摩尔菲尔兹眼科医院）；Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与物理科学研究委员会介入与外科科学中心）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与影像科学学院）
Our work proposes neural network design choices that set the state-of-the-art on a challenging public benchmark on cataract surgery, CaDIS. Our methodology achieves strong performance across three semantic segmentation tasks with increasingly granular surgical tool class sets by effectively handling class imbalance, an inherent challenge in any surgical video. We consider and evaluate two conceptually simple data oversampling methods as well as different loss functions. We show significant performance gains across network architectures and tasks especially on the rarest tool classes, thereby p…
SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院医学机器人研究所）；Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School（哈佛医学院放射科、布里洛女性医院）
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…
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-…
Purpose: This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery. Existing techniques heavily rely on conventional statistic measures or shallow modelings based on hand-engineered features and gesture segmentation. Such developments require significant expert knowledge, are prone to errors, and are less efficient in online adaptive training systems. Methods: In this work, we present an efficient analytic framework with a parallel deep learning architecture, SATR-DL, to assess trainee expertise and rec…
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…
Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery
School of Computation, Information and Technology, Technical University of Munich（计算信息学院，慕尼黑技术大学）；Klinik und Poliklinik für Augenheilkunde, TUM University Hospital（眼科诊所，TUM大学医院）；Computer Aided Medical Procedures, Technical University of Munich（医学辅助程序，慕尼黑技术大学）；Department of Biomedical Engineering, University of Alberta（生物医学工程系，阿尔伯塔大学）
Object Tokens as a Bridge Between Segmentation and Visual Question Answering in Robotic Surgery
Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）；Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电气工程系）；Department of Surgery, University Medical Center Utrecht（乌得勒支大学医学中心外科）
Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery
Dept. of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Dept. of Electrical and Computer Engineering, Vanderbilt University（电气与计算机工程系，范德比尔特大学）
Intuitive, Inc.（直观医疗公司）；Konica Minolta, Inc.（柯尼卡美能达公司）；University College London（伦敦大学学院）；University of Genoa（热那亚大学）；University of Burgundy（勃艮第大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications. However, many challenges exist around surgical data (and health data, more generally) needed to develop context-aware models. This work - presented as part of the Endoscopic Vision (EndoVis) challenge at the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020 conference - seeks to explore the potential for visual domain adaptation in surgery to overcome data privacy concerns. In particular, we propose to use video from virtual reality (VR) simulations of surgical exe…
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 …
Surgical-MambaLLM: Mamba2-enhanced Multimodal Large Language Model for VQLA in Robotic Surgery
Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Imperial College London（帝国理工学院伦敦分校）；The Hong Kong University of Science and Technology（香港科学与技术大学）
SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot
CAIR, HKISI, CAS（中国科学院香港中文大学信息安全研究所智能计算研究中心）；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实验室）；Peking Union Medical College Hospital（北京协和医院）
$\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training
Johns Hopkins University（约翰斯·霍普金斯大学）；Wilmer Eye Institute, Johns Hopkins University（约翰斯·霍普金斯大学威尔默眼科研究所）；Malone Center for Engineering in Healthcare, Johns Hopkins University（约翰斯·霍普金斯大学马龙医疗保健工程中心）
