01.01.2023The Menze group and collaborators publish the liver tumor segmentation benchmark

In this work, the Menze group and collaborators 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. There was not a single algorithm that performed best for both liver and liver tumors in these three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas the best algorithms for tumor segmentation achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, additional analysis on liver tumor detection 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. Nonetheless, LiTS remains an active benchmark and resource for research. See Bilic et al., Med Image Anal.
Both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.