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 adaptation method to improve the performance of the surgical activity recognition model in a new operating room for which we only have unlabeled videos. Our approach generates pseudo labels for unlabeled video clips that it is confident about and trains the model on the augmented version of the clips. We extend our method to a semi-supervised domain adaptation setting where a small portion of the target domain is also labeled. In our experiments, our proposed method consistently outperforms the baselines on a dataset of more than 480 long surgical videos collected from two operating rooms.
Surgical Re-enactment for Operating Room Workflow Datasets
TUM University Hospital（慕尼黑工业大学附属医院）；TUM School of Medicine and Health（慕尼黑工业大学医学与健康学院）；TUM School of Computation, Information, and Technology（慕尼黑工业大学计算、信息与技术学院）；University of Alberta（阿尔伯塔大学）
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…
Activity recognition in surgical videos is a key research area for developing next-generation devices and workflow monitoring systems. Since surgeries are long processes with highly-variable lengths, deep learning models used for surgical videos often consist of a two-stage setup using a backbone and temporal sequence model. In this paper, we investigate many state-of-the-art backbones and temporal models to find architectures that yield the strongest performance for surgical activity recognition. We first benchmark the models performance on a large-scale activity recognition dataset containin…
When do they StOP?: A First Step Towards Automatically Identifying Team Communication in the Operating Room
University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University of Bern（伯尔尼大学）；Clarunis（克拉鲁尼斯）；University of Basel（巴塞尔大学）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；Bern University Hospital（伯尔尼大学医院）
NELVT, Department of Computer Science, Peking University（北京大学计算机科学技术系数字视频编解码技术国家工程实验室）；Deepwise AI Lab（深睿医疗人工智能实验室）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Peking University Cancer Hospital（北京大学肿瘤医院）
Surgical instrument segmentation is a key component in developing context-aware operating rooms. Existing works on this task heavily rely on the supervision of a large amount of labeled data, which involve laborious and expensive human efforts. In contrast, a more affordable unsupervised approach is developed in this paper. To train our model, we first generate anchors as pseudo labels for instruments and background tissues respectively by fusing coarse handcrafted cues. Then a semantic diffusion loss is proposed to resolve the ambiguity in the generated anchors via the feature correlation bet…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
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 of Chicago（芝加哥大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Hefei University of Technology（合肥工业大学）；Shanghai Jiao Tong University（上海交通大学）；Argonne National Laboratory（阿贡国家实验室）；Medtronic（美敦力）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
The Chinese University of Hong Kong（香港中文大学）；Soochow University（苏州大学）；Prince of Wales Hospital（威尔斯亲王医院）
Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. This is currently a bottleneck for the…
University Hospital of Strasbourg（斯特拉斯堡大学医院）；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）
Purpose: Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can indeed help to detect and identify the persons present in the room, and also be used to automatically anonymize the data. However, current algorithms trained on natural images do not generalize well to the operating room (OR) images. In this work, we provide a comparison of state-of-the-art face detectors on OR data and also present an approach to train a face detector for the OR by exploiting non-annotated OR images. Me…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）
Previous work on surgical skill assessment using intraoperative tool motion in the operating room (OR) has focused on highly-structured surgical tasks such as cholecystectomy. Further, these methods only considered generic motion metrics such as time and number of movements, which are of limited instructive value. In this paper, we developed and evaluated an automated approach to the surgical skill assessment of nasal septoplasty in the OR. The obstructed field of view and highly unstructured nature of septoplasty precludes trainees from efficiently learning the procedure. We propose a descrip…
Oxford Brookes University（牛津布鲁克斯大学）；Imperial College London（帝国理工学院）
Nowadays, there are more surgical procedures that are being performed using minimally invasive surgery (MIS). This is due to its many benefits, such as minimal post-operative problems, less bleeding, minor scarring, and a speedy recovery. However, the MIS's constrained field of view, small operating room, and indirect viewing of the operating scene could lead to surgical tools colliding and potentially harming human organs or tissues. Therefore, MIS problems can be considerably reduced, and surgical procedure accuracy and success rates can be increased by using an endoscopic video feed to dete…
Peking University（北京大学）；Peng Cheng Lab（鹏城实验室）；Deepwise AI Lab（深睿医疗人工智能实验室）；Peking University Cancer Hospital（北京大学肿瘤医院）
Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills a…
Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see a recent paradigm shift from supervised learning to self-supervised and/or unsupervised learning approaches that can learn representations from unlabeled datasets. In this paper, we leverage the unlabeled data captured in robotic surgery ORs and propose a novel way to fuse the multi-modal data for a single video frame or image. Instead of producing different augmentations (or 'views') of the same image or video frame…
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…
Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
Chair for Computer Aided Medical Procedures, Technical University of Munich（计算机辅助医疗程序研究所，慕尼黑技术大学）；UCL Hawkes Institute, Dept. Computer Science, University College London（UCL哈维斯研究所，伦敦大学学院计算机科学系）；Department of General, Visceral, and Transplant Surgery, University Hospital, Ludwig-Maximilians-University（普通外科、 visceral外科及移植外科部门，路德维希-马克西米利安大学医院）
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity Recognition
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；Intuitive Surgical Inc.（直觉外科公司）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co, Ltd（Imsight医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simult…
Mitigating Biases in Surgical Operating Rooms with Geometry
Computer Aided Medical Procedures, TU Munich, Germany（图宾根大学医学辅助技术部）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）；UCL Hawkes Institute, Dept. Computer Science, University College London（伦敦大学学院霍克斯研究所）；Minimally Invasive Surgery, University Hospital of Munich (LMU), Germany（慕尼黑大学医院微创外科）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；University of Heidelberg（海德堡大学）；National Center for Tumor Diseases, Partner Site Dresden（国家肿瘤疾病中心德累斯顿分中心）；HIDSS4Health – Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；University of Calabria（卡拉布里亚大学）；understandAI GmbH（understandAI 有限公司）；University of Tübingen（蒂宾根大学）；BMW Group（宝马集团）；KARL STORZ SE & Co. KG（卡尔史托斯公司）；TU Dresden（德累斯顿工业大学）
Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a speci…
TU Munich（慕尼黑工业大学）；University Hospital of Munich (LMU)（慕尼黑大学医院（路德维希-马克西米利安-慕尼黑大学））；John Hopkins University（约翰斯·霍普金斯大学）
The surgical operating room (OR) presents many opportunities for automation and optimization. Videos from various sources in the OR are becoming increasingly available. The medical community seeks to leverage this wealth of data to develop automated methods to advance interventional care, lower costs, and improve overall patient outcomes. Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos. This study demonstrates that surgical action reco…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Purpose: The objective of this investigation is to provide a comprehensive analysis of state-of-the-art methods for video-based assessment of surgical skill in the operating room. Methods: Using a data set of 99 videos of capsulorhexis, a critical step in cataract surgery, we evaluate feature based methods previously developed for surgical skill assessment mostly under benchtop settings. In addition, we present and validate two deep learning methods that directly assess skill using RGB videos. In the first method, we predict instrument tips as keypoints, and learn surgical skill using temporal…
Johns Hopkins University（约翰斯·霍普金斯大学）；Georgia Institute of Technology（佐治亚理工学院）；Université de Montréal（蒙特利尔大学）；Intuitive Surgical Inc.（直观外科公司）
Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness. Methods: Our proposed system is comprised of four Time-of-Flight (ToF) cameras rigidly attached to strategic locations on the daVinci Xi patient side cart (PSC). The cameras are registered to the robot's kinematic chain by performing a one-time calibration routine and therefore, information from all cameras can be fused and represented in one common coordinate frame. Based on this architecture, a multi-view 3D scene semantic segmentati…
Video capture in the surgical operating room (OR) is increasingly possible and has potential for use with computer assisted interventions (CAI), surgical data science and within smart OR integration. Captured video innately carries sensitive information that should not be completely visible in order to preserve the patient's and the clinical teams' identities. When surgical video streams are stored on a server, the videos must be anonymized prior to storage if taken outside of the hospital. In this article, we describe how a deep learning model, Faster R-CNN, can be used for this purpose and h…
TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking
Computer Aided Medical Procedures, TU Munich, Germany（图宾根大学医学辅助技术中心）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）；Minimally Invasive Surgery, University Hospital of Munich (LMU), Germany（慕尼黑大学微创手术中心）
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）；Department for Biomedical Research (DBMR), University of Bern, 3008 Bern, Switzerland（生物医学研究部（DBMR）、伯尔尼大学、瑞士伯尔尼3008、瑞士）；University Hospital of Strasbourg, 67000 Strasbourg, France（斯特拉斯堡大学医院、法国斯特拉斯堡67000、法国）；Department for Visceral Surgery（visceral surgery 部）；Medicine, Bern University Hospital, University of Bern, 3010 Bern, Switzerland（医学、伯尔尼大学医院、伯尔尼大学、瑞士伯尔尼3010、瑞士）
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）
Modern surgeries are performed in complex and dynamic settings, including ever-changing interactions between medical staff, patients, and equipment. The holistic modeling of the operating room (OR) is, therefore, a challenging but essential task, with the potential to optimize the performance of surgical teams and aid in developing new surgical technologies to improve patient outcomes. The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR u…
Sterilizable Scene Graph Generation for Operating Rooms
Technical University of Darmstadt（达姆施塔特工业大学）；ImFusion GmbH（ImFusion有限公司）；Carl Zeiss AG（卡尔蔡司股份公司）；University Medical Center Mainz（美因茨大学医学中心）；Inria Center at University Côte d’Azur（蔚蓝海岸大学法国国家信息与自动化研究所中心）
Technische Universität München（慕尼黑工业大学）；Google（谷歌）；Johns Hopkins University（约翰斯·霍普金斯大学）
From a computer science viewpoint, a surgical domain model needs to be a conceptual one incorporating both behavior and data. It should therefore model actors, devices, tools, their complex interactions and data flow. To capture and model these, we take advantage of the latest computer vision methodologies for generating 3D scene graphs from camera views. We then introduce the Multimodal Semantic Scene Graph (MSSG) which aims at providing a unified symbolic, spatiotemporal and semantic representation of surgical procedures. This methodology aims at modeling the relationship between different c…
Technical University of Munich（慕尼黑工业大学）；Intuitive Surgical Inc.（直觉外科公司）；Stanford University School of Medicine（斯坦福大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different works for the internal analysis have been proposed with the combination of a frame-level and an additional temporal model to address the temporal ambiguities between different workflow phases. For the External recognition task, Clip-level methods are in the focus of researchers targeting the local ambiguities present in the OR scene. In this work we evaluate combinations of different model architectures for the task…
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms
The Chinese University of Hong Kong（香港中文大学）；Technical University of Denmark（丹麦技术大学）；Huazhong University of Science and Technology（华中科技大学）；National University of Singapore（新加坡国立大学）
ICube, University of Strasbourg, CNRS, IHU Strasbourg（ICube实验室，斯特拉斯堡大学，法国国家科学研究中心，斯特拉斯堡大学医院）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. W…
ICube, University of Strasbourg, CNRS, IHU Strasbourg（斯特拉斯堡大学ICube研究所、法国国家科学研究中心、斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg, IRCAD, IHU Strasbourg（斯特拉斯堡大学医院、斯特拉斯堡 IRCAD 研究所、斯特拉斯堡大学医院研究所）
Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR. State-of-the-art surgical phase recognition algorithms using laparoscopic videos are based on fully supervised training. This limits their potential for widespread application, since creation of manual annotations is an expensive process considering the numerous types of existing surgeries and the vast amount of laparoscopic videos available. In this work…
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）；Radiology Department, University Hospital of Strasbourg（斯特拉斯堡大学医院放射科）
Person detection and pose estimation is a key requirement to develop intelligent context-aware assistance systems. To foster the development of human pose estimation methods and their applications in the Operating Room (OR), we release the Multi-View Operating Room (MVOR) dataset, the first public dataset recorded during real clinical interventions. It consists of 732 synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR. It also includes the visual challenges present in such environments, such as occlusions and clutter. We provide camera calibration parameters, color a…
Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Centre for Artificial Intelligence and Robotics, 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）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Wuhan AI Research（武汉人工智能研究院）；ObjectEye Inc.（奥博视科技公司）
SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary
Computer Aided Medical Procedures (CAMP), TU Munich, Germany Munich Center for Machine Learning (MCML), Munich, Germany University of Strasbourg, France The Chinese University of Hong Kong, Hong Kong
The Chinese University of Hong Kong（香港中文大学）；T Stone Robotics Institute, CUHK（香港中文大学T石机器人研究所）
Real-time surgical phase recognition is a fundamental task in modern operating rooms. Previous works tackle this task relying on architectures arranged in spatio-temporal order, however, the supportive benefits of intermediate spatial features are not considered. In this paper, we introduce, for the first time in surgical workflow analysis, Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate surgical phase recognition. Our hybrid embedding aggregation Transformer fuses cleverly designed spatial and temporal embeddings by allowing for active…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Hand gesture is one of the most important means of touchless communication between human and machines. There is a great interest for commanding electronic equipment in surgery rooms by hand gesture for reducing the time of surgery and the potential for infection. There are challenges in implementation of a hand gesture recognition system. It has to fulfill requirements such as high accuracy and fast response. In this paper we introduce a system of hand gesture recognition based on a deep learning approach. Deep learning is known as an accurate detection model, but its high complexity prevents …
Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
Center for Humanoid Research, Korea Institute of Science and Technology（韩国科学技术研究院仿人机器人研究中心）；School of Electrical and Electronic Engineering, Yonsei University（延世大学电气与电子工程学院）
Token Merging via Spatiotemporal Information Mining for Surgical Video Understanding
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology（电子信息与通信学院，华中科技大学）
