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SimCol3D - 3D reconstruction during colonoscopy challenge.
Rau, Anita; Bano, Sophia; Jin, Yueming; Azagra, Pablo; Morlana, Javier; Kader, Rawen; Sanderson, Edward; Matuszewski, Bogdan J; Lee, Jae Young; Lee, Dong-Jae; Posner, Erez; Frank, Netanel; Elangovan, Varshini; Raviteja, Sista; Li, Zhengwen; Liu, Jiquan; Lalithkumar, Seenivasan; Islam, Mobarakol; Ren, Hongliang; Lovat, Laurence B; Montiel, José M M; Stoyanov, Danail.
Afiliación
  • Rau A; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK; Stanford University, Stanford, CA, USA. Electronic address: arau@stanford.edu.
  • Bano S; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK. Electronic address: sophia.bano@ucl.ac.uk.
  • Jin Y; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK; National University of Singapore, Singapore. Electronic address: ymjin@nus.edu.sg.
  • Azagra P; University of Zaragoza, Zaragoza, Spain.
  • Morlana J; University of Zaragoza, Zaragoza, Spain.
  • Kader R; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK.
  • Sanderson E; Computer Vision and Machine Learning (CVML) Group, University of Central Lancashire, Preston, UK.
  • Matuszewski BJ; Computer Vision and Machine Learning (CVML) Group, University of Central Lancashire, Preston, UK.
  • Lee JY; Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Lee DJ; Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Posner E; Intuitive Surgical, USA.
  • Frank N; Intuitive Surgical, USA.
  • Elangovan V; College of Engineering, Guindy, India.
  • Raviteja S; Indian Institute of Technology Kharagpur, Kharagpur, India.
  • Li Z; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China.
  • Liu J; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China.
  • Lalithkumar S; National University of Singapore, Singapore; The Chinese University of Hong Kong, Hong Kong, China.
  • Islam M; Imperial College London, London, UK.
  • Ren H; National University of Singapore, Singapore; The Chinese University of Hong Kong, Hong Kong, China.
  • Lovat LB; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK.
  • Montiel JMM; University of Zaragoza, Zaragoza, Spain.
  • Stoyanov D; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK.
Med Image Anal ; 96: 103195, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38815359
ABSTRACT
Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Colonoscopía / Imagenología Tridimensional Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Colonoscopía / Imagenología Tridimensional Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos