mlsanityData to 14 Oct 2026

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

arXiv 2010.14808 ↗First posted 28 Oct 2020cs.CVcited by 224

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Abstract (arXiv)

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. The datasets, codes, and trained models are publicly available at https://github.com/JunMa11/AbdomenCT-1K.

Key facts. Cited by 224 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2020.

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Citing papers per year: 2021: 11; 2022: 16; 2023: 52; 2024: 68; 2025: 57; 2026: 20.

224 papers.

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  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
  2. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  3. 2024M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Modelscited by 162
  4. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 100
  5. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  6. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  7. 2023UniverSeg: Universal Medical Image Segmentationcited by 91
  8. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 87
  9. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 76
  10. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 65
  11. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 64
  12. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
38 more
  1. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 55
  2. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  3. 2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
  4. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  5. 2024Segment Anything in Medical Images and Videos: Benchmark and Deploymentcited by 42
  6. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 41
  7. 2023Segment Anything Model for Medical Images?cited by 39
  8. 2023One-Prompt to Segment All Medical Imagescited by 38
  9. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  10. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 36
  11. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 36
  12. 2023SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentationcited by 35
  13. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 32
  14. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  15. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  16. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  17. 2021Multi-Anchor Active Domain Adaptation for Semantic Segmentationcited by 29
  18. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 29
  19. 2023Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysiscited by 26
  20. 2023Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labelingcited by 26
  21. 2023MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Datasetcited by 26
  22. 2025MedDINOv3: How to adapt vision foundation models for medical image segmentation?cited by 25
  23. 2025Triad: Vision Foundation Model for 3D Magnetic Resonance Imagingcited by 24
  24. 2023SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstructioncited by 23
  25. 2024Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmarkcited by 23
  26. 2021Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentationcited by 22
  27. 2022MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recoverycited by 22
  28. 2023Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Surveycited by 21
  29. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  30. 2025MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Losscited by 20
  31. 2022Learning Incrementally to Segment Multiple Organs in a CT Imagecited by 19
  32. 2023SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularizationcited by 19
  33. 2024Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging casescited by 19
  34. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  35. 2023Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentationcited by 17
  36. 2023Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelinescited by 17
  37. 2023COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-trainingcited by 16
  38. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 16

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