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The Liver Tumor Segmentation Benchmark (LiTS).

In Medical image analysis

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.

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, Hesser Jürgen, Moltz Jan Hendrik, Igel Christian, Isensee Fabian, Jäger 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

2022-Nov-17

Benchmark, CT, Deep learning, Liver, Liver tumor, Segmentation