ArticleIEEE transactions on medical imaging2021
Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Physics-based simulation of ultrasound propagation from MRI volumes for cross-modal breast imaging.Journal of biological engineering · 2026Article
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Clinical Assessment of Fine-Tuned Open-Source LLMs in Cardiology: From Progress Notes to Discharge Summary.Journal of healthcare informatics research · 2025Article
- Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.Scientific reports · 2025Article
- SATU-net: a shadow adaptive tracing U-net for gastric cavity segmentation based on the principle of ultrasound imaging.Scientific reports · 2025Article
- Analysis of intra- and inter-observer variability in 4D liver ultrasound landmark labeling.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound.NPJ digital medicine · 2025Article
- New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.European heart journal. Imaging methods and practice · 2025Article
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Cardiac ultrasound simulation for autonomous ultrasound navigation.Frontiers in cardiovascular medicine · 2024Article
- MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- [Research progress on medical image dataset expansion methods].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- An Overview of Deep Learning Methods for Left Ventricle Segmentation.Computational intelligence and neuroscience · 2023Review
- Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation.Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online) · 2023Article
- MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging.Frontiers in cardiovascular medicine · 2022Article
- Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views.Simplifying medical ultrasound : third International Workshop, ASMUS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. ASMUS (Workshop) (3rd : 2022 : Singapore) · 2022Article
- Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.IEEE transactions on medical imaging · 2021Article
- Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta.Frontiers in physiology · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic 2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. A convolutional neural network is trained to segment the left ventricle and left atrium using only synthetic images. Networks trained with synthetic images were extensively tested on four different unseen datasets of real images with median Dice scores of 91, 90, 88, and 87 for left ventricle segmentation. These results match or are better than inter-observer results measured on real ultrasound datasets and are comparable to a network trained on a separate set of real images. Results demonstrate the images produced can effectively be used in place of real data for training. The proposed pipeline opens the door for automatic generation of training data for many tasks in medical imaging as the same process can be applied to other segmentation or landmark detection tasks in any modality. The source code and anatomical models are available to other researchers.
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