Traditional open-access datasets focusing on surgical procedures are often limited by their small size, typically consisting of fewer than 100 videos and less than 30 hours of footage, which leads to poor model generalization. To address this data limitation, a new dataset called LEMON has been compiled using a novel aggregation pipeline that collects high-resolution videos from online sources. Featuring an extensive collection of over 4K surgical videos totaling 938 hours (85 million frames) of high-quality footage across multiple procedure types, LEMON offers a comprehensive resource surpassing existing alternatives in size and scope, including two novel downstream tasks. To demonstrate the effectiveness of this diverse dataset, we introduce LemonFM, a foundation model pretrained on LEMON using a novel self-supervised augmented knowledge distillation approach. LemonFM consistently outperforms existing surgical foundation models across four downstream tasks and six datasets, achieving significant gains in surgical phase recognition (+9.5pp, +9.4pp, and +8.4pp in Jaccard on AutoLaparo, M2CAI16, and Cholec80), surgical action recognition (+4.4pp in mAP on CholecT50), surgical tool presence detection (+5.3pp and +10.2pp in mAP on Cholec80 and GraSP), and surgical semantic segmentation (+10.3pp in mDice on CholecSeg8k). LEMON and LemonFM will serve as foundational resources for the research community and industry, accelerating progress in developing autonomous robotic surgery systems and ultimately contributing to safer and more accessible surgical care worldwide. Dataset, code, and models are publicly available at https://github.com/visurg-ai/LEMON.
TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation
Birmingham City University（伯明翰城市大学）；University Hospitals Birmingham（伯明翰大学医院）；University of Birmingham（伯明翰大学）；University of the West of England（西英格兰大学）；University of Hertfordshire（赫特福德大学）；University of Bristol（布里斯托大学）；Qatar University（卡塔尔大学）
ICube, University of Strasbourg, CNRS（斯特拉斯堡大学ICube研究所，法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会IRCCS）；IRCAD France（法国消化癌症研究研究所）
Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, presented as <instrument, verb, target> combinations, is highly challenging to be accurately identified. Triplet components can be difficult to recognize individually; in this task, it requires not only performing recognition simultaneously for all three triplet components, but also correctly establishing the data association between them. To achieve …
Zuse Institute Berlin (ZIB)（柏林祖斯研究所）；TU Darmstadt（达姆施塔特工业大学）
Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods: We introduce a teacher-student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the erroneous learning problem of the current consistency-based unsupervised domain adaptation framework. Results: Empirical results on three datasets highlig…
Geo-RepNet: Geometry-Aware Representation Learning for Surgical Phase Recognition in Endoscopic Submucosal Dissection
Department of Electronic Engineering, The Chinese University of Hong Kong（电子工程系，香港中文大学）；Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.（理论实验室，中央研究院，2012实验室，华为技术有限公司）
Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
organization= German Cancer Research Center (DKFZ) Heidelberg, Div. Intelligent Medical Systems , country= Germany；organization= National Center for Tumor Diseases (NCT), NCT Heidelberg , country= Germany；Data Science School for Health , country= Germany；organization= Medical Faculty, Heidelberg University , country= Germany；organization= Faculty of Mathematics；Computer Science, Heidelberg University , country= Germany；organization= Weldon School of Biomedical Engineering, Purdue University , city= West Lafayette , state= IN , country= USA；organization= Regenstrief Center for Healthcare Engineering (RCHE), Purdue University , city= West Lafayette , state= IN , country= USA；organization= Department of Biostatistics；Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University School of Medicine , city= Indianapolis , state= IN , country= USA；organization= Department of Surgery, Indiana University School of Medicine , city= Indianapolis , state= IN , country= USA；organization= Department of Visceral, Thoracic；Vascular Surgery, University Hospital；Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology , city= Dresden , country= Germany
Oxford Brookes University（牛津布鲁克斯大学）；ETH Zurich（苏黎世联邦理工学院）；San Raffaele Hospital（圣拉斐尔医院）；Harbin Institute of Technology（哈尔滨工业大学）；Osaka University（大阪大学）；The George Washington University（乔治华盛顿大学）；University of Verona（维罗纳大学）
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar structure of the surgical scene, the greater similarity in appearance of actions performed via tools in a cavity compared to, say, human actions in unconstrained environments, as well as from the motion of the endoscopic camera. This paper presents ESAD, the first large-scale dataset designed to tackle the problem of surgeon action detection in endoscopic minimally invasive surgery. ESAD aims at contributing to increase th…
ETH Zurich（苏黎世联邦理工学院）；San Raffaele Hospital（圣拉斐尔医院）；University of Verona（维罗纳大学）；Oxford Brookes University（牛津布鲁克斯大学）
In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real-world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which…
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院）；Politecnico di Milano（米兰理工大学）
Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information an…
Politecnico di Milano（米兰理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University Hospital（海德堡大学医院）
Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral i…
Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging. The lack of rigorous investigation into the topic combined with the lack of a common benchmark dataset for this task has been a long-lasting issue in the field. In this paper, we propose two variants of a lean GPU-ba…
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）
F2PASeg: Feature Fusion for Pituitary Anatomy Segmentation in Endoscopic Surgery
1Centre for Artificial Intelligence；Robotics, Hong Kong Institute of Science \& Innovation, Chinese Academy of Sciences 2The University of Hong Kong 3Peking Union Medical College Hospital, Chinese Academy of Medical Science
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…
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 …
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）
Benchmarking Endoscopic Surgical Image Restoration and Beyond
The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；Southern Medical University（南方医科大学）；Xidian University（西安电子科技大学）；Wuhan University（武汉大学）
Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting dow…
University of Washington (UW)（华盛顿大学）；Rochester Institute of Technology（罗切斯特理工学院）
Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-…
EndoLive: Real-Time Style Transfer for Endoscopic Endonasal Skull Base Surgical Video
University of Pittsburgh（匹兹堡大学）；University of Pittsburgh School of Computing and Information（匹兹堡大学计算与信息学院）；School of Computing and Information（计算与信息学院）
University of Washington（华盛顿大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Deep learning-based methods have achieved promising results on surgical instrument segmentation. However, the high computation cost may limit the application of deep models to time-sensitive tasks such as online surgical video analysis for robotic-assisted surgery. Moreover, current methods may still suffer from challenging conditions in surgical images such as various lighting conditions and the presence of blood. We propose a novel Multi-frame Feature Aggregation (MFFA) module to aggregate video frame features temporally and spatially in a recurrent mode. By distributing the computation load…
SAGS: Self-Adaptive Alias-Free Gaussian Splatting for Dynamic Surgical Endoscopic Reconstruction
University of Technology Sydney, Australia（悉尼技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所）；Shenyang Institute of Automation, Chinese Academy of Sciences, China（沈阳自动化研究所）
CPKD: Clinical Prior Knowledge-Constrained Diffusion Models for Surgical Phase Recognition in Endoscopic Submucosal Dissection
School of Biomedical Engineering（生物医学工程学院）；Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, Renji Hospital（消化内科与肝病科、上海消化疾病研究所、国家消化疾病临床医学研究中心、仁济医院）；College of Media Engineering（媒体工程学院）；Aier Institute of Digital Ophthalmology and Visual Science（数字眼科与视觉科学研究院）
Pixel-Wise Recognition for Holistic Surgical Scene Understanding
Center for Research and Formation in Artificial Intelligence (CinfonIA)（人工智能研究与形成中心（CinfonIA））；Universidad de los Andes（安第斯大学）；Fundación Santa Fe de Bogotá（圣菲波哥大基金会）
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Beijing Institute of Technology（北京理工大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Tongji University（同济大学）；University College London（伦敦大学学院）
Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos
IHU Strasbourg（斯特拉斯堡大学医院研究所）；University of Bern（伯尔尼大学）；Inselspital, Bern University Hospital（伯尔尼大学医院因塞尔医院）；University of Strasbourg（斯特拉斯堡大学）；ICube（ICube实验室）；CNRS（法国国家科学研究中心）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（天主教大学 Gemelli 医院基金会）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
ICube（ICube实验室）；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；IRCAD（法国消化外科研究所）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会IRCCS）
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room. In this work, we tackle the recognition of fine-grained activities, modeled as action triplets <instrument, verb, target> representing the tool activity. To this end, we introduce a new laparoscopic dataset, CholecT40, consisting of 40 videos from the public dataset Cholec80 in which all frames have been annotated using 128 triplet classes. Furthermore, we present an approach to recognize these triplets directly from the video data. It relies on a module called Class Act…
Xiamen University（厦门大学）；The Chinese University of Hong Kong（香港中文大学）；Zhongshan Hospital affiliated to Xiamen University（厦门大学附属中山医院）；The Hong Kong Polytechnic University（香港理工大学）
We propose a novel shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection (ESD) surgery. This task is of great clinical significance but extremely challenging due to bleeding, lighting reflection, and motion blur in the complicated surgical environment. Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking…
SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba
School of Biomedical Engineering（生物医学工程学院）；Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, Renji Hospital（消化内科与肝病科、上海消化疾病研究所、国家消化疾病临床医学研究中心、仁济医院）；Aier Institute of Digital Ophthalmology and Visual Science（爱尔数字眼科与视觉科学研究院）；College of Media Engineering（媒体工程学院）
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）
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection
The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Qilu Hospital of Shandong University（山东大学齐鲁医院）；National University o（国立大学）
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）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, the application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information…
National Center for Tumor Diseases（国家肿瘤疾病中心）；University of Heidelberg（海德堡大学）；KARL STORZ SE & Co. KG（卡尔史托斯股份两合公司）；TU Dresden（德累斯顿工业大学）
Purpose The course of surgical procedures is often unpredictable, making it difficult to estimate the duration of procedures beforehand. A context-aware method that analyses the workflow of an intervention online and automatically predicts the remaining duration would alleviate these problems. As basis for such an estimate, information regarding the current state of the intervention is required. Methods Today, the operating room contains a diverse range of sensors. During laparoscopic interventions, the endoscopic video stream is an ideal source of such information. Extracting quantitative inf…
State-Change Learning for Prediction of Future Events in Endoscopic Videos
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, France（斯特拉斯堡大学，法国国家科学研究中心，法国国家医学研究院，ICube，UMR7357，法国）；University Hospital of Strasbourg, France（斯特拉斯堡大学医院，法国）
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…
Indian Institute of Technology Delhi（印度理工学院德里分校）；All India Institute of Medical Sciences（全印度医学科学研究所）
Neuro-endoscopy is a challenging minimally invasive neurosurgery that requires surgical skills to be acquired using training methods different from the existing apprenticeship model. There are various training systems developed for imparting fundamental technical skills in laparoscopy where as limited systems for neuro-endoscopy. Neuro-Endo-Trainer was a box-trainer developed for endo-nasal transsphenoidal surgical skills training with video based offline evaluation system. The objective of the current study was to develop a modified version (Neuro-Endo-Trainer-Online Assessment System (NET-OA…
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
The Chinese University of Hong Kong (CUHK)（香港中文大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）
Registering Neural 4D Gaussians for Endoscopic Surgery
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）；KTH, Kungliga Tekniska högskolan Royal Institute of Technology（瑞典皇家理工学院）；Hong Kong Applied Science and Technology Research Institute Company Limited（香港应用科技研究院有限公司）
Northwick Park Hospital（诺斯威克公园医院）；German Cancer Research Center（德国癌症研究中心）；Imperial College London（帝国理工学院）；University College London（伦敦大学学院）
Intra-operative measurements of tissue shape and multi/ hyperspectral information have the potential to provide surgical guidance and decision making support. We report an optical probe based system to combine sparse hyperspectral measurements and spectrally-encoded structured lighting (SL) for surface measurements. The system provides informative signals for navigation with a surgical interface. By rapidly switching between SL and white light (WL) modes, SL information is combined with structure-from-motion (SfM) from white light images, based on SURF feature detection and Lucas-Kanade (LK) o…
Surgical robots are used to perform minimally invasive surgery and alleviate much of the burden imposed on surgeons. Our group has developed a surgical robot to aid in the removal of tumors at the base of the skull via access through the nostrils. To avoid injuring the patients, a collision-avoidance algorithm that depends on having an accurate model for the poses of the instruments' shafts is used. Given that the model's parameters can change over time owing to interactions between instruments and other disturbances, the online estimation of the poses of the instrument's shaft is essential. I…
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL（伦敦大学学院威康/EPSRC介入与外科科学中心）；National Center for Tumor Diseases (NCT) Dresden（德累斯顿国家肿瘤疾病中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）
In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom images or biological tissue samples covering a relatively small region of the endoscope field-of-view. We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT). Two {\it ex vivo} small porcine full torso cadav…
