mlsanityData to 7 Oct 2026

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

arXiv 1904.00445 ↗First posted 31 Mar 2019q-bio.QM cs.LG stat.MLcited by 168

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Abstract (arXiv)

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.

Key facts. Cited by 168 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2019.

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Citing papers per year: 2021: 28; 2022: 20; 2023: 36; 2024: 32; 2025: 37; 2026: 15.

168 papers.

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  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  2. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  3. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
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  5. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  6. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  7. 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
  8. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  9. 2021nnDetection: A Self-configuring Method for Medical Object Detectioncited by 60
  10. 2021FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysiscited by 60
  11. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  12. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
38 more
  1. 2023MultiTalent: A Multi-Dataset Approach to Medical Image Segmentationcited by 45
  2. 2021Efficient Medical Image Segmentation Based on Knowledge Distillationcited by 43
  3. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
  4. 2023CLIP in Medical Imaging: A Surveycited by 40
  5. 2023Segment Anything Model for Medical Images?cited by 39
  6. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  7. 2021Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentationcited by 34
  8. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 34
  9. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  10. 2021Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotationscited by 31
  11. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  12. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  13. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  14. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  15. 2023CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolutioncited by 28
  16. 2022Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Reviewcited by 27
  17. 2021All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentationcited by 24
  18. 2022Detailed Annotations of Chest X-Rays via CT Projection for Report Understandingcited by 24
  19. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  20. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  21. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 22
  22. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  23. 2021Label-Assemble: Leveraging Multiple Datasets with Partial Labelscited by 20
  24. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  25. 2022Learning Incrementally to Segment Multiple Organs in a CT Imagecited by 19
  26. 2022Learning from partially labeled data for multi-organ and tumor segmentationcited by 18
  27. 2021Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentationcited by 17
  28. 2021Free-form tumor synthesis in computed tomography images via richer generative adversarial networkcited by 16
  29. 2022Federated Multi-organ Segmentation with Inconsistent Labelscited by 15
  30. 2023Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentationcited by 15
  31. 2025Show and Segment: Universal Medical Image Segmentation via In-Context Learningcited by 15
  32. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 15
  33. 2021Evaluating the Robustness of Self-Supervised Learning in Medical Imagingcited by 13
  34. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  35. 2021Task-Oriented Low-Dose CT Image Denoisingcited by 11
  36. 2023Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approachcited by 11
  37. 2025Learning Segmentation from Radiology Reportscited by 11
  38. 2022Universal Segmentation of 33 Anatomiescited by 10

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