The Liver Tumor Segmentation Benchmark (LiTS)
Abstract
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}.
Keywords
Cite
@article{arxiv.1901.04056,
title = {The Liver Tumor Segmentation Benchmark (LiTS)},
author = {Patrick Bilic and Patrick Christ and Hongwei Bran Li and Eugene Vorontsov and Avi Ben-Cohen and Georgios Kaissis and Adi Szeskin and Colin Jacobs and Gabriel Efrain Humpire Mamani and Gabriel Chartrand and Fabian Lohöfer and Julian Walter Holch and Wieland Sommer and Felix Hofmann and Alexandre Hostettler and Naama Lev-Cohain and Michal Drozdzal and Michal Marianne Amitai and Refael Vivantik and Jacob Sosna and Ivan Ezhov and Anjany Sekuboyina and Fernando Navarro and Florian Kofler and Johannes C. Paetzold and Suprosanna Shit and Xiaobin Hu and Jana Lipková and Markus Rempfler and Marie Piraud and Jan Kirschke and Benedikt Wiestler and Zhiheng Zhang and Christian Hülsemeyer and Marcel Beetz and Florian Ettlinger and Michela Antonelli and Woong Bae and Míriam Bellver and Lei Bi and Hao Chen and Grzegorz Chlebus and Erik B. Dam and Qi Dou and Chi-Wing Fu and Bogdan Georgescu and Xavier Giró-i-Nieto and Felix Gruen and Xu Han and Pheng-Ann Heng and Jürgen Hesser and Jan Hendrik Moltz and Christian Igel and Fabian Isensee and Paul Jäger and Fucang Jia and Krishna Chaitanya Kaluva and Mahendra Khened and Ildoo Kim and Jae-Hun Kim and Sungwoong Kim and Simon Kohl and Tomasz Konopczynski and Avinash Kori and Ganapathy Krishnamurthi and Fan Li and Hongchao Li and Junbo Li and Xiaomeng Li and John Lowengrub and Jun Ma and Klaus Maier-Hein and Kevis-Kokitsi Maninis and Hans Meine and Dorit Merhof and Akshay Pai and Mathias Perslev and Jens Petersen and Jordi Pont-Tuset and Jin Qi and Xiaojuan Qi and Oliver Rippel and Karsten Roth and Ignacio Sarasua and Andrea Schenk and Zengming Shen and Jordi Torres and Christian Wachinger and Chunliang Wang and Leon Weninger and Jianrong Wu and Daguang Xu and Xiaoping Yang and Simon Chun-Ho Yu and Yading Yuan and Miao Yu and Liping Zhang and Jorge Cardoso and Spyridon Bakas and Rickmer Braren and Volker Heinemann and Christopher Pal and An Tang and Samuel Kadoury and Luc Soler and Bram van Ginneken and Hayit Greenspan and Leo Joskowicz and Bjoern Menze},
journal= {arXiv preprint arXiv:1901.04056},
year = {2022}
}
Comments
Patrick Bilic, Patrick Christ, Hongwei Bran Li, and Eugene Vorontsov made equal contributions to this work. Published in Medical Image Analysis