CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
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）；Technical University Munich（慕尼黑工业大学）；Riwolink GmbH（Riwolink有限公司）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Shanghai Jiao Tong University（上海交通大学）；IPCA（葡萄牙卡瓦多和阿韦河理工学院）；University of Minho（米尼奥大学）
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma, Aditya Murali, Deepak Alapatt, Armine Vardazaryan, Kun Yuan, Jonas Hajek, Wolfgang Reiter, Amine Yamlahi, Finn-H… 展开作者
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma, Aditya Murali, Deepak Alapatt, Armine Vardazaryan, Kun Yuan, Jonas Hajek, Wolfgang Reiter, Amine Yamlahi, Finn-Henri Smidt, Xiaoyang Zou, Guoyan Zheng, Bruno Oliveira, Helena R. Torres, Satoshi Kondo, Satoshi Kasai, Felix Holm, Ege Özsoy, Shuangchun Gui, Han Li, Sista Raviteja, Rachana Sathish, Pranav Poudel, Binod Bhattarai, Ziheng Wang, Guo Rui, Melanie Schellenberg, João L. Vilaça, Tobias Czempiel, Zhenkun Wang, Debdoot Sheet, Shrawan Kumar Thapa, Max Berniker, Patrick Godau, Pedro Morais, Sudarshan Regmi, Thuy Nuong Tran, Jaime Fonseca, Jan-Hinrich Nölke, Estevão Lima, Eduard Vazquez, Lena Maier-Hein, Nassir Navab, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Didier Mutter, Nicolas Padoy
备注：
MICCAI EndoVis CholecTriplet2022 challenge report. Published at Elsevier journal of Medical Image Analysis. 25 pages, 15 figures, 8 tables
发表信息：
Medical Image Analysis, Volume 89, 2023, 102888, ISSN 1361-8415
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of <instrument, verb, target> triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.
SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics
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）
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Information Technology University（信息技术大学）；University of Bristol（布里斯托尔大学）；Bristol Heart Institute（布里斯托尔心脏研究所）；University of the West of England（西英格兰大学）；Qatar University（卡塔尔大学）
Surgical Video Motion Magnification with Suppression of Instrument Artefacts
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院威康/EPSRC介入与外科科学中心）；Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery（国立神经病学与神经外科医院神经外科）
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion
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）；The University of Sydney（悉尼大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
3D endoscopic depth estimation using 3D surface-aware constraints
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）
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
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）
EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control
organization= Department of Electronic Engineering, The Chinese University of Hong Kong , state= Hong Kong SAR , country= China；organization= The Chinese University of Hong Kong Shenzhen Research Institute , city= Shen Zhen , state= Guangdong , country= China；organization= The First Affiliated Hospital of Wenzhou Medical University , city= Wenzhou , state= Zhejiang , country= China；organization= Department of General Surgery \& Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University , city= Guang Zhou , state= Guangdong , country= China；organization= Department of Otolaryngology-Head；Neck Surgery, Singapore General Hospital, Duke-NUS Medical School , country= Singapore；organization= Shenzhen Key Laboratory of Robotics Perception；the Department of Electronic；Electrical Engineering, Southern University of Science
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
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）
