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Bilic, P.* ; Christ, P.* ; Li, H.B.* ; Vorontsov, E.A.* ; 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.* ; Lipkova, 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.*

The Liver Tumor Segmentation Benchmark (LiTS).

Med. Image Anal. 84:102680 (2022)
Postprint DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publication type Article: Journal article
Document type Scientific Article
Keywords Benchmark ; Ct ; Deep Learning ; Liver ; Liver Tumor ; Segmentation; Statistical Shape Model; Surgical Resection; Ct; Burden; Metastases; Cancer; Algorithm; Lesions; Recist; Tissue
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Journal Medical Image Analysis
Quellenangaben Volume: 84, Issue: , Pages: , Article Number: 102680 Supplement: ,
Publisher Elsevier
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530001-001
G-505800-001
Grants Forschungskredit from University of Zurich
Fondation de l'association des radiologistes du Qubec
Fonds de recherche du Qubec en Sante
Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE)
Helmut-Horten-Foundation
DFG
DOI 10.1016/j.media.2022.102680
WOS ID 000928244200004
PubMed ID 36481607
Erfassungsdatum 2022-12-19
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