mlsanityData to 7 Oct 2026

The Liver Tumor Segmentation Benchmark (LiTS)

arXiv 1901.04056 ↗First posted 13 Jan 2019cs.CVcited by 504

Not on the map. Why?

Abstract (arXiv)

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 urlhttp://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via urlwww.lits-challenge.com.

Key facts. Cited by 504 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jan 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 60; 2022: 62; 2023: 107; 2024: 105; 2025: 109; 2026: 61.

504 papers.

Sort:

By area:

  1. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  2. 2021The Medical Segmentation Decathloncited by 620
  3. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  4. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  5. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  6. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  7. 2024Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomographycited by 192
  8. 2024M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Modelscited by 154
  9. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  10. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 109
  11. 2023The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTcited by 101
  12. 2023UniverSeg: Universal Medical Image Segmentationcited by 90
38 more
  1. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  2. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  4. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  5. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  6. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  7. 20233DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationcited by 70
  8. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  9. 2021Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learningcited by 68
  10. 2024Towards Generalizable Tumor Synthesiscited by 65
  11. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  12. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  13. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 59
  14. 2023Understanding metric-related pitfalls in image analysis validationcited by 57
  15. 2023Label-Free Liver Tumor Segmentationcited by 56
  16. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  17. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentationcited by 54
  18. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  19. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  20. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  21. 2022DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysiscited by 48
  22. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  23. 2023UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learnercited by 46
  24. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  25. 2023Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasetscited by 45
  26. 2021Efficient Medical Image Segmentation Based on Knowledge Distillationcited by 43
  27. 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspectivecited by 43
  28. 2023Continual Learning for Abdominal Multi-Organ and Tumor Segmentationcited by 43
  29. 2024Segment Anything in Medical Images and Videos: Benchmark and Deploymentcited by 42
  30. 2022Box-supervised Instance Segmentation with Level Set Evolutioncited by 41
  31. 2023PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysiscited by 40
  32. 2023CLIP in Medical Imaging: A Surveycited by 40
  33. 2023Segment Anything Model for Medical Images?cited by 39
  34. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  35. 2023Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learningcited by 34
  36. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 34
  37. 2021Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentationcited by 33
  38. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33

50 most cited of 504 All 504 (JSON)