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The Liver Tumor Segmentation Benchmark (LiTS).
Bilic, Patrick; Christ, Patrick; Li, Hongwei Bran; Vorontsov, Eugene; Ben-Cohen, Avi; Kaissis, Georgios; Szeskin, Adi; Jacobs, Colin; Mamani, Gabriel Efrain Humpire; Chartrand, Gabriel; Lohöfer, Fabian; Holch, Julian Walter; Sommer, Wieland; Hofmann, Felix; Hostettler, Alexandre; Lev-Cohain, Naama; Drozdzal, Michal; Amitai, Michal Marianne; Vivanti, Refael; Sosna, Jacob; Ezhov, Ivan; Sekuboyina, Anjany; Navarro, Fernando; Kofler, Florian; Paetzold, Johannes C; Shit, Suprosanna; Hu, Xiaobin; Lipková, Jana; Rempfler, Markus; Piraud, Marie; Kirschke, Jan; Wiestler, Benedikt; Zhang, Zhiheng; Hülsemeyer, Christian; Beetz, Marcel; Ettlinger, Florian; Antonelli, Michela; Bae, Woong; Bellver, Míriam; Bi, Lei; Chen, Hao; Chlebus, Grzegorz; Dam, Erik B; Dou, Qi; Fu, Chi-Wing; Georgescu, Bogdan; Giró-I-Nieto, Xavier; Gruen, Felix; Han, Xu; Heng, Pheng-Ann.
Afiliación
  • Bilic P; Department of Informatics, Technical University of Munich, Germany.
  • Christ P; Department of Informatics, Technical University of Munich, Germany.
  • Li HB; Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland. Electronic address: hongwei.li@tum.de.
  • Vorontsov E; Ecole Polytechnique de Montréal, Canada.
  • Ben-Cohen A; Department of Biomedical Engineering, Tel-Aviv University, Israel.
  • Kaissis G; Institute for AI in Medicine, Technical University of Munich, Germany; Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom.
  • Szeskin A; School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel.
  • Jacobs C; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Mamani GEH; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Chartrand G; The University of Montréal Hospital Research Centre (CRCHUM) Montréal, Québec, Canada.
  • Lohöfer F; Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany.
  • Holch JW; Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; Comprehensive Cancer Center Munich, Munich, Germany; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Sommer W; Department of Radiology, University Hospital, LMU Munich, Germany.
  • Hofmann F; Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Germany; Department of Radiology, University Hospital, LMU Munich, Germany.
  • Hostettler A; Department of Surgical Data Science, Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD), France.
  • Lev-Cohain N; Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel.
  • Drozdzal M; Polytechnique Montréal, Mila, QC, Canada.
  • Amitai MM; Department of Diagnostic Radiology, Sheba Medical Center, Tel Aviv university, Israel.
  • Vivanti R; Rafael Advanced Defense System, Israel.
  • Sosna J; Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel.
  • Ezhov I; Department of Informatics, Technical University of Munich, Germany.
  • Sekuboyina A; Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland.
  • Navarro F; Department of Informatics, Technical University of Munich, Germany; Department of Radiation Oncology and Radiotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany.
  • Kofler F; Department of Informatics, Technical University of Munich, Germany; Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany; Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany; TranslaTUM - Central Institute for Translati
  • Paetzold JC; Department of Computing, Imperial College London, London, United Kingdom; Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
  • Shit S; Department of Informatics, Technical University of Munich, Germany.
  • Hu X; Department of Informatics, Technical University of Munich, Germany.
  • Lipková J; Brigham and Women's Hospital, Harvard Medical School, USA.
  • Rempfler M; Department of Informatics, Technical University of Munich, Germany.
  • Piraud M; Department of Informatics, Technical University of Munich, Germany; Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany.
  • Kirschke J; Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany.
  • Wiestler B; Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany.
  • Zhang Z; Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, China.
  • Hülsemeyer C; Department of Informatics, Technical University of Munich, Germany.
  • Beetz M; Department of Informatics, Technical University of Munich, Germany.
  • Ettlinger F; Department of Informatics, Technical University of Munich, Germany.
  • Antonelli M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Bae W; Kakao Brain, Republic of Korea.
  • Bellver M; Barcelona Supercomputing Center, Barcelona, Spain.
  • Bi L; School of Computer Science, the University of Sydney, Australia.
  • Chen H; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China.
  • Chlebus G; Fraunhofer MEVIS, Bremen, Germany; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Dam EB; Department of Computer Science, University of Copenhagen, Denmark.
  • Dou Q; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
  • Fu CW; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
  • Georgescu B; Siemens Healthineers, USA.
  • Giró-I-Nieto X; Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Catalonia, Spain.
  • Gruen F; Institute of Control Engineering, Technische Universität Braunschweig, Germany.
  • Han X; Department of computer science, UNC Chapel Hill, USA.
  • Heng PA; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Med Image Anal ; 84: 102680, 2023 02.
Article en En | MEDLINE | ID: mdl-36481607
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Benchmarking / Neoplasias Hepáticas Tipo de estudio: Observational_studies Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Benchmarking / Neoplasias Hepáticas Tipo de estudio: Observational_studies Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos