The Liver Tumor Segmentation Benchmark (LiTS)

Autor: Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yue, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
Přispěvatelé: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CROMAI - Computing Resources Orchestration and Management for AI
Jazyk: angličtina
Rok vydání: 2022
Předmět:
FOS: Computer and information sciences
Radiological and Ultrasound Technology
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Health Informatics
Deep learning
Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo [Àrees temàtiques de la UPC]
Benchmark
Computer Graphics and Computer-Aided Design
Informàtica::Aplicacions de la informàtica [Àrees temàtiques de la UPC]
Liver -- Cancer
Segmentation
All institutes and research themes of the Radboud University Medical Center
Liver
Ct
Deep Learning
Liver Tumor
Diagnostic imaging
Fetge -- Càncer
Radiology
Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Liver tumor
Imatgeria per al diagnòstic
CT
Aprenentatge profund
Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Zdroj: Med. Image Anal. 84:102680 (2022)
Medical Image Analysis, 84
Bilic, P, Christ, P, Li, H B, Vorontsov, E, Ben-Cohen, A, Kaissis, G, Szeskin, A, Jacobs, C, Mamani, G E H, Chartrand, G, Lohöfer, F, Holch, J W, Sommer, W, Hofmann, F, Hostettler, A, Lev-Cohain, N, Drozdzal, M, Amitai, M M, Vivanti, R, Sosna, J, Ezhov, I, Sekuboyina, A, Navarro, F, Kofler, F, Paetzold, J C, Shit, S, Hu, X, Lipková, J, Rempfler, M, Piraud, M, Kirschke, J, Wiestler, B, Zhang, Z, Hülsemeyer, C, Beetz, M, Ettlinger, F, Antonelli, M, Bae, W, Bellver, M, Bi, L, Chen, H, Chlebus, G, Dam, E B, Dou, Q, Fu, C-W, Georgescu, B, Giró-I-Nieto, X, Gruen, F, Han, X, Heng, P-A, Hesser, J, Moltz, J H, Igel, C, Isensee, F, Jäger, P, Jia, F, Kaluva, K C, Khened, M, Kim, I, Kim, J-H, Kim, S, Kohl, S, Konopczynski, T, Kori, A, Krishnamurthi, G, Li, F, Li, H, Li, J, Li, X, Lowengrub, J, Ma, J, Maier-Hein, K, Maninis, K-K, Meine, H, Merhof, D, Pai, A, Perslev, M, Petersen, J, Pont-Tuset, J, Qi, J, Qi, X, Rippel, O, Roth, K, Sarasua, I, Schenk, A, Shen, Z, Torres, J, Wachinger, C, Wang, C, Weninger, L, Wu, J, Xu, D, Yang, X, Yu, S C-H, Yuan, Y, Yue, M, Zhang, L, Cardoso, J, Bakas, S, Braren, R, Heinemann, V, Pal, C, Tang, A, Kadoury, S, Soler, L, van Ginneken, B, Greenspan, H, Joskowicz, L & Menze, B 2023, ' The Liver Tumor Segmentation Benchmark (LiTS) ', Medical Image Analysis, vol. 84, 102680 . https://doi.org/10.1016/j.media.2022.102680
ISSN: 1361-8415
DOI: 10.1016/j.media.2022.102680
Popis: 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 \url{http://medicaldecathlon.com/}. In addition, both data and online evaluation are accessible via \url{www.lits-challenge.com}.
Patrick Bilic, Patrick Christ, Hongwei Bran Li, and Eugene Vorontsov made equal contributions to this work. Published in Medical Image Analysis
Databáze: OpenAIRE
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