mlsanityData to 7 Oct 2026

MAISI: Medical AI for Synthetic Imaging

arXiv 2409.11169 ↗First posted 13 Sep 2024eess.IV cs.AI cs.CVcited by 86

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 44 of its 85 references that are in the map. First posted on arXiv 13 Sep 2024.

Builds on

44 of 85 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models7 Feb
  2. 2023Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research15 Nov
  3. 2023GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes25 May
  4. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar
  5. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction5 Mar

Other references · 39

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  9. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  10. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  11. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
  12. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
27 more
  1. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,339
  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  4. 2022Diffusion Models in Vision: A Surveycited by 1,008
  5. 2019An Introduction to Variational Autoencoderscited by 997
  6. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  7. 2021The Medical Segmentation Decathloncited by 620
  8. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  9. 2017Generative Adversarial Networks: An Overviewcited by 588
  10. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  11. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  12. 2022A Survey on Generative Diffusion Modelcited by 238
  13. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  14. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  15. 2018Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Networkcited by 97
  16. 2018CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)cited by 81
  17. 20203D Deep Learning on Medical Images: A Reviewcited by 76
  18. 2018SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truthcited by 68
  19. 2024Towards Generalizable Tumor Synthesiscited by 65
  20. 2021GANs for Medical Image Synthesis: An Empirical Studycited by 64
  21. 2020Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANcited by 60
  22. 2020SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesiscited by 37
  23. 2019mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesiscited by 34
  24. 2021Label-Assemble: Leveraging Multiple Datasets with Partial Labelscited by 20
  25. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  26. 2022Synthetic Tumors Make AI Segment Tumors Bettercited by 14
  27. 2024FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesiscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 43; 2026: 39.

86 papers.

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  1. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  2. 2025MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Losscited by 20
  3. 2025Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imagingcited by 20
  4. 2025MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairscited by 13
  5. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  6. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  7. 2025Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Freecited by 9
  8. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applicationscited by 7
  9. 2025From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generationcited by 6
  10. 2025CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesiscited by 5
  11. 2025CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesiscited by 5
  12. 2025Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Codecited by 4
38 more
  1. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 4
  2. 2025Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modelingcited by 4
  3. 2024MRGen: Segmentation Data Engine for Underrepresented MRI Modalitiescited by 3
  4. 2025Automating RT Planning at Scale: High Quality Data For AI Trainingcited by 3
  5. 2025Diffusion Models for conditional MRI generationcited by 3
  6. 2025MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesiscited by 3
  7. 2025Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Modelcited by 3
  8. 2025Uncertainty-Aware ControlNet: Bridging Domain Gaps with Synthetic Image Generationcited by 3
  9. 2026Scalable next-scale autoregression for medical image generation across anatomical regionscited by 3
  10. 2026MAESIL: Masked Autoencoder for Enhanced Self-supervised Medical Image Learningcited by 3
  11. 2024Artificial Intelligence for Biomedical Video Generationcited by 2
  12. 2025Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Mapcited by 2
  13. 2025High Volume Rate 3D Ultrasound Reconstruction with Diffusion Modelscited by 2
  14. 2025NeRF-based CBCT Reconstruction needs Normalization and Initializationcited by 2
  15. 2025MedSymmFlow: Bridging Generative Modeling and Classification in Medical Imaging through Symmetrical Flow Matchingcited by 2
  16. 2025Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesiscited by 2
  17. 2025Diffusion-Driven Generation of Minimally Preprocessed Brain MRIcited by 2
  18. 2025GRASP: Guided Residual Adapters with Sample-wise Partitioningcited by 2
  19. 2025NodMAISI: Nodule-Oriented Medical AI for Synthetic Imagingcited by 2
  20. 2026Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributionscited by 2
  21. 2026TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growthcited by 2
  22. 2025Virtual Patients, Real Gains: Digital Twin-Based Simulated CT for Multitask Lung Nodule Analysiscited by 1
  23. 2025Interactive Tumor Progression Modeling via Sketch-Based Image Editingcited by 1
  24. 2025Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Modelscited by 1
  25. 2025Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surfacecited by 1
  26. 2025Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generationcited by 1
  27. 2025PRO: Projection Domain Synthesis for CT Imagingcited by 1
  28. 2025Paired Image Generation with Diffusion-Guided Diffusion Modelscited by 1
  29. 2025SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Modelcited by 1
  30. 2025MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Frameworkcited by 1
  31. 2026Cortex-Grounded Diffusion Models for Brain Image Generationcited by 1
  32. 2026Retrieval-Augmented Anatomical Guidance for Text-to-CT Generationcited by 1
  33. 2026Sparse Representation Learning for Vesselscited by 1
  34. 2026Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attentioncited by 1
  35. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Studycited by 1
  36. 2026EgoInteract: Synthetic Egocentric Videos Generation for Interaction Understanding and Anticipationcited by 1
  37. 2026Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRIcited by 1
  38. 2025StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesiscited by 0

50 most cited of 86 All 86 (JSON)

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  1. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  2. 2023Generative AI for Medical Imaging: extending the MONAI Framework
  3. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
  4. 2024Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
  5. 2024XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
  6. 2025seg2med: a bridge from artificial anatomy to multimodal medical images
  7. 2025Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
  8. 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation