  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}.
