mlsanityData to 14 Oct 2026

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

arXiv 2307.01984 ↗First posted 5 Jul 2023cs.CV cs.AI cs.LGcited by 103

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Abstract (arXiv)

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.

Key facts. Cited by 103 papers in the map (data through 14 Oct 2026). Builds on 12 of its 55 references that are in the map. First posted on arXiv 5 Jul 2023.

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12 of 55 references.

References · 12

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  4. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  5. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 190
  6. 2020VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Imagescited by 111
  7. 2018Why rankings of biomedical image analysis competitions should be interpreted with carecited by 91
  8. 2018Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapycited by 77
  9. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60
  10. 2022Labeling instructions matter in biomedical image analysiscited by 20
  11. 2018Imperfect Segmentation Labels: How Much Do They Matter?cited by 14
  12. 2019Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learningcited by 2

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Citing papers per year: 2023: 6; 2024: 25; 2025: 45; 2026: 27.

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  4. 2023SAM-Med2Dcited by 124
  5. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 107
  6. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 87
  7. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
  8. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
  9. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 41
  10. 2025QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Trainingcited by 38
  11. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 36
  12. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
38 more
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  2. 2024The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomographycited by 26
  3. 2025MedDINOv3: How to adapt vision foundation models for medical image segmentation?cited by 25
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  5. 2025Comprehensive language-image pre-training for 3D medical image understandingcited by 22
  6. 2024MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAMcited by 17
  7. 2024FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesiscited by 15
  8. 2025Curia: A Multi-Modal Foundation Model for Radiologycited by 15
  9. 2025CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Modelscited by 13
  10. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  11. 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregationcited by 12
  12. 2025LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imagingcited by 12
  13. 2025UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretationcited by 10
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  15. 2025Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challengecited by 8
  16. 2025MediSee: Reasoning-based Pixel-level Perception in Medical Imagescited by 8
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  18. 2024MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentationcited by 7
  19. 2024Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignmentcited by 7
  20. 2025Towards Universal Text-driven CT Image Segmentationcited by 7
  21. 2024SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Promptscited by 5
  22. 2024ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluationcited by 5
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  24. 2025MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentationcited by 5
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  27. 2025LesionDiffusion: Towards Text-controlled General Lesion Synthesiscited by 4
  28. 2025Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Studycited by 3
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  30. 2025Self-Supervised Learning for Image Segmentation: A Comprehensive Surveycited by 3
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  38. 2025The Missing Piece: A Case for Pre-Training in 3D Medical Object Detectioncited by 2

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