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CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.
Nwoye, Chinedu Innocent; Yu, Tong; Sharma, Saurav; Murali, Aditya; Alapatt, Deepak; Vardazaryan, Armine; Yuan, Kun; Hajek, Jonas; Reiter, Wolfgang; Yamlahi, Amine; Smidt, Finn-Henri; Zou, Xiaoyang; Zheng, Guoyan; Oliveira, Bruno; Torres, Helena R; Kondo, Satoshi; Kasai, Satoshi; Holm, Felix; Özsoy, Ege; Gui, Shuangchun; Li, Han; Raviteja, Sista; Sathish, Rachana; Poudel, Pranav; Bhattarai, Binod; Wang, Ziheng; Rui, Guo; Schellenberg, Melanie; Vilaça, João L; Czempiel, Tobias; Wang, Zhenkun; Sheet, Debdoot; Thapa, Shrawan Kumar; Berniker, Max; Godau, Patrick; Morais, Pedro; Regmi, Sudarshan; Tran, Thuy Nuong; Fonseca, Jaime; Nölke, Jan-Hinrich; Lima, Estevão; Vazquez, Eduard; Maier-Hein, Lena; Navab, Nassir; Mascagni, Pietro; Seeliger, Barbara; Gonzalez, Cristians; Mutter, Didier; Padoy, Nicolas.
Afiliação
  • Nwoye CI; ICube, University of Strasbourg, CNRS, France. Electronic address: nwoye@unistra.fr.
  • Yu T; ICube, University of Strasbourg, CNRS, France.
  • Sharma S; ICube, University of Strasbourg, CNRS, France.
  • Murali A; ICube, University of Strasbourg, CNRS, France.
  • Alapatt D; ICube, University of Strasbourg, CNRS, France.
  • Vardazaryan A; ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, France.
  • Yuan K; ICube, University of Strasbourg, CNRS, France; Technical University Munich, Germany.
  • Hajek J; Riwolink GmbH, Germany.
  • Reiter W; Riwolink GmbH, Germany.
  • Yamlahi A; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Smidt FH; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Zou X; Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, China.
  • Zheng G; Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, China.
  • Oliveira B; 2Ai School of Technology, IPCA, Barcelos, Portugal; Life and Health Science Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Algoritimi Center, School of Engineering, University of Minho, Guimeraes, Portugal.
  • Torres HR; 2Ai School of Technology, IPCA, Barcelos, Portugal; Life and Health Science Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Algoritimi Center, School of Engineering, University of Minho, Guimeraes, Portugal.
  • Kondo S; Muroran Institute of Technology, Japan.
  • Kasai S; Niigata University of Health and Welfare, Japan.
  • Holm F; Technical University Munich, Germany.
  • Özsoy E; Technical University Munich, Germany.
  • Gui S; Southern University of Science and Technology, China.
  • Li H; Southern University of Science and Technology, China.
  • Raviteja S; Indian Institute of Technology, Kharagpur, India.
  • Sathish R; Indian Institute of Technology, Kharagpur, India.
  • Poudel P; Redev Technology Ltd, UK.
  • Bhattarai B; University College, London, UK; University of Aberdeen, UK.
  • Wang Z; Intuitive Surgical, USA.
  • Rui G; Intuitive Surgical, USA.
  • Schellenberg M; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany.
  • Vilaça JL; 2Ai School of Technology, IPCA, Barcelos, Portugal.
  • Czempiel T; Technical University Munich, Germany.
  • Wang Z; Southern University of Science and Technology, China.
  • Sheet D; Indian Institute of Technology, Kharagpur, India.
  • Thapa SK; Nepal Applied Mathematics and Informatics Institute for research (NAAMII), Nepal.
  • Berniker M; Intuitive Surgical, USA.
  • Godau P; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany.
  • Morais P; 2Ai School of Technology, IPCA, Barcelos, Portugal.
  • Regmi S; Nepal Applied Mathematics and Informatics Institute for research (NAAMII), Nepal.
  • Tran TN; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Fonseca J; Algoritimi Center, School of Engineering, University of Minho, Guimeraes, Portugal.
  • Nölke JH; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany.
  • Lima E; Life and Health Science Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
  • Vazquez E; Redev Technology Ltd, UK.
  • Maier-Hein L; Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Navab N; Technical University Munich, Germany.
  • Mascagni P; Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
  • Seeliger B; ICube, University of Strasbourg, CNRS, France; University Hospital of Strasbourg, France; IHU Strasbourg, France.
  • Gonzalez C; University Hospital of Strasbourg, France; IHU Strasbourg, France.
  • Mutter D; University Hospital of Strasbourg, France; IHU Strasbourg, France.
  • Padoy N; ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, France.
Med Image Anal ; 89: 102888, 2023 10.
Article em En | MEDLINE | ID: mdl-37451133
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 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Cirurgia Assistida por Computador Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Med Image Anal Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Cirurgia Assistida por Computador Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Med Image Anal Ano de publicação: 2023 Tipo de documento: Article