The Liver Tumor Segmentation Benchmark (LiTS).
The Liver Tumor Segmentation Benchmark (LiTS).
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DOI：
10.1016/j.media.2022.102680
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发表时间：
2023-02
影响因子：
10.9
通讯作者：
Menze, Bjoern
中科院分区：
文献类型：
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作者：
Bilic, Patrick;Christ, Patrick;Li, Hongwei Bran;Vorontsov, Eugene;Ben-Cohen, Avi;Kaissis, Georgios;Szeskin, Adi;Jacobs, Colin;Mamani, Gabriel Efrain Humpire;Chartrand, Gabriel;Lohoefer, 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;Lipkova, Jana;Rempfler, Markus;Piraud, Marie;Kirschke, Jan;Wiestler, Benedikt;Zhang, Zhiheng;Huelsemeyer, Christian;Beetz, Marcel;Ettlinger, Florian;Antonelli, Michela;Bae, Woong;Bellver, Miriam;Bi, Lei;Chen, Hao;Chlebus, Grzegorz;Dam, Erik B.;Dou, Qi;Fu, Chi-Wing;Georgescu, Bogdan;Giro-I-Nieto, Xavier;Gruen, Felix;Han, Xu;Heng, Pheng-Ann;Hesser, Jurgen;Moltz, Jan Hendrik;Igel, Christian;Isensee, Fabian;Jaeger, Paul;Jia, Fucang;Kaluva, Krishna Chaitanya;Khened, Mahendra;Kim, Ildoo;Kim, Jae-Hun;Kim, Sungwoong;Kohl, Simon;Konopczynski, Tomasz;Kori, Avinash;Krishnamurthi, Ganapathy;Li, Fan;Li, Hongchao;Li, Junbo;Li, Xiaomeng;Lowengrub, John;Ma, Jun;Maier-Hein, Klaus;Maninis, Kevis-Kokitsi;Meine, Hans;Merhof, Dorit;Pai, Akshay;Perslev, Mathias;Petersen, Jens;Pont-Tuset, Jordi;Qi, Jin;Qi, Xiaojuan;Rippel, Oliver;Roth, Karsten;Sarasua, Ignacio;Schenk, Andrea;Shen, Zengming;Torres, Jordi;Wachinger, Christian;Wang, Chunliang;Weninger, Leon;Wu, Jianrong;Xu, Daguang;Yang, Xiaoping;Yu, Simon Chun-Ho;Yuan, Yading;Yue, Miao;Zhang, Liping;Cardoso, Jorge;Bakas, Spyridon;Braren, Rickmer;Heinemann, Volker;Pal, Christopher;Tang, An;Kadoury, Samuel;Soler, Luc;van Ginneken, Bram;Greenspan, Hayit;Joskowicz, Leo;Menze, Bjoern
在这项工作中，我们报告了肝脏肿瘤分割基准（LiTS）的设置和结果，该基准是与2017年IEEE国际生物医学成像研讨会（ISBI）以及2017年和2018年医学图像计算和计算机辅助干预国际会议（MICCAI）联合组织的。图像数据集是多样化的，包含不同大小和外观的原发性和继发性肿瘤，具有不同的病变背景水平（高密度/低密度），与七家医院和研究机构合作创建。在一组131个计算机断层扫描（CT）体积上训练了75个提交的肝脏和肝脏肿瘤分割算法，并在从不同患者采集的70个看不见的测试图像上进行了测试。我们发现，在这三个事件中，没有一个算法对肝脏和肝脏肿瘤的表现最好。最佳肝脏分割算法的Dice评分为0.963，而对于肿瘤分割，最佳算法的Dice评分为0.674（ISBI 2017）、0.702（MICCAI 2017）和0.739（MICCAI 2018）。回顾过去，我们对肝脏肿瘤检测进行了额外的分析，并发现并非所有表现最好的分割算法都能很好地用于肿瘤检测。最佳肝脏肿瘤检测方法的病灶召回率为0.458（ISBI 2017）、0.515（MICCAI 2017）和0.554（MICCAI 2018），表明需要进一步研究。LiTS仍然是一个活跃的基准和研究资源，例如，在http://medicaldecathlon.com/中贡献肝脏相关的分割任务。此外，可通过https://competitions.codalab.org/competitions/17094查阅数据和在线评价。
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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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
DOI：
10.1007/978-3-030-59710-8_14
发表时间：
2020-10
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Haghighi, Fatemeh;Hosseinzadeh Taher, Mohammad Reza;Zhou, Zongwei;Gotway, Michael B;Liang, Jianming
通讯作者：
Liang, Jianming
影响因子：
168.9
作者：
Albain, Kathy S.;Swann, R. Suzanne;Rusch, Valerie W.;Turrisi, Andrew T., III;Shepherd, Frances A.;Smith, Colum;Chen, Yuhchyau;Livingston, Robert B.;Feins, Richard H.;Gandara, David R.;Fry, Willard A.;Darling, Gail;Johnson, David H.;Green, Mark R.;Miller, Robert C.;Ley, Joanne;Sause, Willliam T.;Cox, James D.
通讯作者：
Cox, James D.
DOI：
10.1007/s11548-006-0059-z
发表时间：
2007-02-01
影响因子：
3
作者：
Bornemann, Lars;Dicken, Volker;Peitgen, Heinz-Otto
通讯作者：
Peitgen, Heinz-Otto
影响因子：
16.6
作者：
通讯作者：
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