mlsanityData to 7 Oct 2026

AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking

arXiv 2407.16697 ↗First posted 23 Jul 2024cs.CVcited by 64

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Abstract (arXiv)

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms -- the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 44 of its 69 references that are in the map. First posted on arXiv 23 Jul 2024.

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44 of 69 references.

References · 44

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  10. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
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  12. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 282
32 more
  1. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  2. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
  3. 2023Sequential Modeling Enables Scalable Learning for Large Vision Modelscited by 212
  4. 2019Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysiscited by 159
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  11. 2020DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetscited by 94
  12. 2024Towards Generalizable Tumor Synthesiscited by 65
  13. 2023Label-Free Liver Tumor Segmentationcited by 56
  14. 2021UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barriercited by 55
  15. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  16. 2018Abdominal multi-organ segmentation with organ-attention networks and statistical fusioncited by 46
  17. 2019Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localizationcited by 38
  18. 2021SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detectioncited by 34
  19. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  20. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  21. 2023Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelinescited by 17
  22. 2023Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesiscited by 17
  23. 2024Shortcut Learning in Medical Image Segmentationcited by 16
  24. 2023Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-trainingcited by 15
  25. 2022Synthetic Tumors Make AI Segment Tumors Bettercited by 14
  26. 2024Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Imagescited by 13
  27. 2024Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scanscited by 10
  28. 2023Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Datacited by 9
  29. 2023Synthetic Data as Validationcited by 9
  30. 2024Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?cited by 9
  31. 2022Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site datacited by 5
  32. 2023A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 35; 2026: 20.

64 papers.

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  1. 2025Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Modelscited by 144
  2. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 34
  3. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  4. 2024Learning General-Purpose Biomedical Volume Representations using Randomized Synthesiscited by 27
  5. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 22
  6. 2024Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challengecited by 20
  7. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 15
  8. 2024ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomicscited by 13
  9. 2025MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairscited by 13
  10. 2025Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Modelscited by 13
  11. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  12. 2024Analyzing Tumors by Synthesiscited by 11
38 more
  1. 2025Learning Segmentation from Radiology Reportscited by 11
  2. 2025Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Maskscited by 8
  3. 2025Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge resultscited by 7
  4. 2025MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Datasetcited by 6
  5. 2025Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?cited by 6
  6. 2025Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformerscited by 6
  7. 2025RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answeringcited by 6
  8. 2026Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Modelscited by 6
  9. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  10. 2025X-LRM: X-ray Large Reconstruction Model for Extremely Sparse-View Computed Tomography Recovery in One Secondcited by 5
  11. 2025X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomographycited by 5
  12. 2025MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentationcited by 5
  13. 2024Label Critic: Design Data Before Modelscited by 4
  14. 2024Efficient MedSAMs: Segment Anything in Medical Images on Laptopcited by 4
  15. 2026RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiologycited by 4
  16. 2025Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentationcited by 3
  17. 2025See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancementcited by 3
  18. 2024Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projectioncited by 2
  19. 2025The Missing Piece: A Case for Pre-Training in 3D Medical Object Detectioncited by 2
  20. 2025Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?cited by 2
  21. 2025TAP-CT: 3D Task-Agnostic Pretraining of Computed Tomography Foundation Modelscited by 2
  22. 2026Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Datacited by 2
  23. 2026MedTri: A Platform for Structured Medical Report Normalization to Enhance Vision-Language Pretrainingcited by 2
  24. 2026Learning Clinical Representations Under Systematic Distribution Shiftcited by 2
  25. 2026Discriminative Representation Learning for Clinical Predictioncited by 2
  26. 2025Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representationscited by 1
  27. 2025Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapycited by 1
  28. 2025State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicabilitycited by 1
  29. 2025Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Frameworkcited by 1
  30. 2026Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomographycited by 1
  31. 2026Uncertainty-Aware Foundation Models for Clinical Datacited by 1
  32. 2026GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Taskscited by 1
  33. 2024Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imagingcited by 0
  34. 2025AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalitiescited by 0
  35. 2025BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Datacited by 0
  36. 2025Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasetscited by 0
  37. 2025ShapeKitcited by 0
  38. 2025RAPS-3D: Efficient interactive segmentation for 3D radiological imagingcited by 0

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