mlsanityData to 7 Oct 2026

Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation

arXiv 2305.05424 ↗First posted 9 May 2023eess.IV cs.CV cs.LGcited by 33

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6 ± 4.91 , 91.9 ± 4.22, 85.2 ± 4.83 % for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of 9.2, 3.3 and 13.9 % in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 12 of its 23 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

12 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 243
  7. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
  8. 2022Semantic Image Synthesis via Diffusion Modelscited by 131
  9. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  10. 2019Unsupervised Medical Image Translation Using Cycle-MedGANcited by 44
  11. 2022Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo viewscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 15; 2025: 12; 2026: 3.

33 papers.

Sort:

By area:

  1. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  2. 2025Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Qualitycited by 24
  3. 2023Dehazing Ultrasound using Diffusion Modelscited by 17
  4. 2025EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidancecited by 13
  5. 2025Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Modelscited by 12
  6. 2024Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generationcited by 9
  7. 2024XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Modelcited by 8
  8. 2023Echocardiography video synthesis from end diastolic semantic map via diffusion modelcited by 7
  9. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
  10. 2023Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiographycited by 6
  11. 2024Vision-Language Synthetic Data Enhances Echocardiography Downstream Taskscited by 6
  12. 2024Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesiscited by 6
21 more
  1. 2024Sequential Posterior Sampling with Diffusion Modelscited by 6
  2. 2025Ultrasound Image Generation using Latent Diffusion Modelscited by 6
  3. 2025Toward Medical Deepfake Detection: A Comprehensive Dataset and Novel Methodcited by 6
  4. 2024CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentationcited by 5
  5. 2024Cardiac ultrasound simulation for autonomous ultrasound navigationcited by 4
  6. 2025EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generationcited by 4
  7. 2024Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimescited by 3
  8. 2025Generative augmentations for improved cardiac ultrasound segmentation using diffusion modelscited by 3
  9. 2024MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipelinecited by 2
  10. 2025High Volume Rate 3D Ultrasound Reconstruction with Diffusion Modelscited by 2
  11. 2025From Transthoracic to Transesophageal: Cross-Modality Generation using LoRA Diffusioncited by 2
  12. 2024S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonographycited by 1
  13. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiogramscited by 1
  14. 2025PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentationcited by 1
  15. 2024Synthetic Simplicity: Unveiling Bias in Medical Data Augmentationcited by 0
  16. 2024DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Imagescited by 0
  17. 2025Generative deep learning for foundational video translation in ultrasoundcited by 0
  18. 2025Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesiscited by 0
  19. 2026Feature-Space Guided Diffusion for Realistic Ultrasound Image Synthesiscited by 0
  20. 2026Physics-Guided Synthetic High-Frequency Ultrasound Generation for Skin Layer Segmentationcited by 0
  21. 2026AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Echo-DND: A dual noise diffusion model for robust and precise left ventricle segmentation in echocardiography
  2. 2023Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation
  3. 2023Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
  4. 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
  5. 2024A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images
  6. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  7. 2023Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
  8. 2025Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?