RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
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）
Andru Putra Twinanda, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
备注：
10 pages, IEEE Transactions on Medical Imaging, 2018
Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos. Previous state-of-the-art approaches for RSD prediction are dependent on manual annotation, whose generation requires expensive expert knowledge and is time-consuming, especially considering the numerous types of surgeries performed in a hospital and the large number of laparoscopic videos available. A crucial feature of RSDNet is that it does not depend on any manual annotation during training, making it easily scalable to many kinds of surgeries. The generalizability of our approach is demonstrated by testing the pipeline on two large datasets containing different types of surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental results also show that the proposed network significantly outperforms a traditional method of estimating RSD without utilizing manual annotation. Further, this work provides a deeper insight into the deep learning network through visualization and interpretation of the features that are automatically learned.
CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery
Kings College London, Surgical & Interventional Engineering（伦敦国王学院，外科与介入工程学院）；Kings College Hospital Denmark Hill, department（伦敦国王学院丹尼希尔医院，部门）；Department of Informatics, King’s College London（国王学院信息学院）；Tongji University, College of Electronic and Information Engineering（同济大学，电子与信息工程学院）
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
hutom；Yonsei University College of Medicine（延世大学医学院）
手术器械的自动定位是了解手术过程并对其进行分析从而为外科医生提供有意义的术中指导或术后手术指标的重要技术。我们引入了一个新的数据集，反映了手术器械的运动学特征，用于手术视频的自动手术器械定位。hSDB（hutom Surgery DataBase）-仪器数据集由24例腹腔镜胆囊切除术和24例机器人胃切除术的仪器定位信息组成。所有仪器的定位信息以用于目标检测的限界框的形式提供。为了处理仪器之间的类别不平衡问题，在Unity中为3D模型建模的合成仪器被包括作为训练数据。此外，对于3D仪器数据，提供了多边形注释以实现工具的实例分割。为了反映所有器械的运动学特征，腹腔镜器械用头部和身体部位注释，机器人器械分别用头部、手腕和身体部位注释。辅助工具（标本袋、针头等）标注数据经常用于外科手术的也包括在内。此外，我们提供了统计信息的hSDB仪器数据集和基线定位性能的目标检测网络训练的MMDetection库和分析结果。
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?
Norwegian University of Science & Technology（挪威科技大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Paris 13（巴黎第十三大学）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）
Attachment Anchors: A Novel Framework for Laparoscopic Grasping Point Prediction in Colorectal Surgery
Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Department of Surgery, Research Group MITI（慕尼黑技术大学，TUM医学与健康学院，TUM慕尼黑大学医院rechts der Isar，外科部，MITI研究组）；Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Chair for AI in Healthcare and Medicine Munich（慕尼黑技术大学，TUM医学与健康学院，TUM慕尼黑大学医院rechts der Isar，人工智能在医疗与健康中的chair）；Department of Computing, Imperial College London（伦敦帝国学院计算系）
Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
Biomedical Robotics Lab, Istituto Italiano di Tecnologia, Genoa, Italy（生物医学机器人实验室，意大利理工学院，热那亚）；Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy（计算机科学、生物工程、机器人与系统工程系（DIBRIS），热那亚大学，意大利）；Harvard Medical School, Brigham and Women’s Hospital, Boston, USA（哈佛医学院，布里洛妇女医院，美国波士顿）
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）
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）
Future-State Predicting LSTM for Early Surgery Type Recognition
ICube, University of Strasbourg, CNRS, IHU Strasbourg（斯特拉斯堡大学ICube、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（斯特拉斯堡大学医院）；IRCAD（消化癌症外科研究所）；IHU Strasbourg（斯特拉斯堡大学医院研究所）
Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection
The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；National University of Singapore, Singapore（新加坡国立大学）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）；Nanfang Hospital, Southern Medical University, Guangzhou, China（南方医科大学附属南华医院）
Feature Mixing Approach for Detecting Intraoperative Adverse Events in Laparoscopic Roux-en-Y Gastric Bypass Surgery
ICube UMR7357 CNRS INSERM University of Strasbourg France（法国斯特拉斯堡大学ICube研究所）；University Digestive Health Care Center, Clarunis, Basel, Switzerland（瑞士巴塞尔大学消化健康中心）；Dept. of Biomedical Engineering, University of Basel, Switzerland（瑞士巴塞尔大学生物医学工程系）；IHU Strasbourg France（法国斯特拉斯堡IHU）
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）
Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院威康/EPSRC介入与外科科学中心）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
A benchmark for video-based laparoscopic skill analysis and assessment
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院）；TUD Dresden University of Technology（德累斯顿工业大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI)（人在环触觉互联网中心）；Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学医学院暨卡尔·古斯塔夫·卡鲁斯大学医院）；Municipal Hospital Dresden-Friedrichstadt（德累斯顿-弗里德里希施塔特市立医院）
Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning
Institutes of Innovation for Future Society（未来社会创新研究所）；Nagoya University（名古屋大学）；Department of Micro-Nano Mechanical Science and Engineering（微纳米机械科学与工程系）
Live Laparoscopic Video Retrieval with Compressed Uncertainty
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）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅里大学综合诊所基金会IRCCS）；IRCAD（法国消化外科研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
A 4D Representation for Training-Free Agentic Reasoning from Monocular Laparoscopic Video
Computer Aided Medical Procedures, TU Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Hospital of the LMU Munich, Ludwig-Maximilians-Universität (LMU)（慕尼黑大学医院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
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）
Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching
Center for Imaging Science and Department of Biomedical Engineering, Rochester Institute of Technology（成像科学中心和生物医学工程系，罗切斯特理工学院）；Department of Biomedical Engineering, Vanderbilt University（生物医学工程系，范德比尔特大学）；School of Science and Engineering, University of Missouri – Kansas City（科学与工程学院，密苏里大学-堪萨斯城分校）
