mlsanityData to 14 Oct 2026

Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis

arXiv 2204.06929 ↗First posted 14 Apr 2022eess.IV cs.CV cs.LGcited by 42

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Abstract (arXiv)

Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical diagnosis. The training of new sonographers and deep learning based algorithms for US image analysis usually requires a large amount of data. However, obtaining and labeling large-scale US imaging data are not easy tasks, especially for diseases with low incidence. Realistic US image synthesis can alleviate this problem to a great extent. In this paper, we propose a generative adversarial network (GAN) based image synthesis framework. Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN. We superpose the edge sketch onto the object mask and use the composite mask as the network input; 3) to generate high-resolution US images, we adopt a progressive training strategy to gradually generate high-resolution images from low-resolution images. In addition, a feature loss is proposed to minimize the difference of high-level features between the generated and real images, which further improves the quality of generated images; 4) the proposed US image synthesis method is quite universal and can also be generalized to the US images of other anatomical structures besides the three ones tested in our study (lung, hip joint, and ovary); 5) extensive experiments on three large US image datasets are conducted to validate our method. Ablation studies, customized texture editing, user studies, and segmentation tests demonstrate promising results of our method in synthesizing realistic US images.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 14 Apr 2022.

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22 of 46 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  6. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  7. 2014Conditional Generative Adversarial Netscited by 2,162
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  9. 2018Demystifying MMD GANscited by 1,293
  10. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  11. 2017The Perception-Distortion Tradeoffcited by 587
  12. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
10 more
  1. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  2. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  3. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  4. 2017Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networkscited by 15
  5. 2019SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesiscited by 14
  6. 2017Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learningcited by 13
  7. 2020Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developmentscited by 11
  8. 2017Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Resultscited by 9
  9. 2018SUSAN: Segment Unannotated image Structure using Adversarial Networkcited by 9
  10. 2020Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 11; 2024: 12; 2025: 9; 2026: 5.

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  1. 2023Segment Anything Model for Medical Images?cited by 39
  2. 2023Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesiscited by 32
  3. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  4. 2024EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharingcited by 18
  5. 2023Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotationscited by 17
  6. 2024Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNNcited by 15
  7. 2023Fourier Test-time Adaptation with Multi-level Consistency for Robust Classificationcited by 12
  8. 2022Online Reflective Learning for Robust Medical Image Segmentationcited by 8
  9. 2023Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentationscited by 8
  10. 2023Echocardiography video synthesis from end diastolic semantic map via diffusion modelcited by 7
  11. 2022Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decouplingcited by 6
  12. 2024Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesiscited by 6
30 more
  1. 2025FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesiscited by 5
  2. 2025EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generationcited by 5
  3. 2022Fine-grained Correlation Loss for Regressioncited by 4
  4. 2023DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Imagescited by 4
  5. 2024Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classificationcited by 4
  6. 2025Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Modelcited by 4
  7. 2022Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasoundcited by 3
  8. 2023Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videoscited by 3
  9. 2025Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasoundcited by 3
  10. 2025MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstructioncited by 3
  11. 2026Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Educationcited by 3
  12. 2022A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformercited by 2
  13. 2024High-Quality Medical Image Generation from Free-hand Sketchcited by 2
  14. 2024MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipelinecited by 2
  15. 2024S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonographycited by 2
  16. 2024CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generationcited by 2
  17. 2025Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasoundcited by 2
  18. 2023OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesiscited by 1
  19. 2023FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentationcited by 1
  20. 2023SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networkscited by 1
  21. 2024SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Modelscited by 1
  22. 2024DAFT-GAN: Dual Affine Transformation Generative Adversarial Network for Text-Guided Image Inpaintingcited by 1
  23. 2025MReg: A Novel Regression Model with MoE-based Video Feature Mining for Mitral Regurgitation Diagnosiscited by 1
  24. 2024End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinogramscited by 0
  25. 2025Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measurecited by 0
  26. 2025Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluationcited by 0
  27. 2026Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networkscited by 0
  28. 2026Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correctioncited by 0
  29. 2026AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesiscited by 0
  30. 2026Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translationcited by 0