# SinGAN-Seg: Synthetic training data generation for medical image segmentation

arXiv: https://arxiv.org/abs/2107.00471 · HTML: https://mlsanity.com/paper/2107.00471/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 72 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy](https://mlsanity.com/paper/2104.11138/) (2021)
- [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https://mlsanity.com/paper/1709.00938/) (2017)
- [Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach](https://mlsanity.com/paper/2010.07467/) (2020)
- [Robustness study of noisy annotation in deep learning based medical image segmentation](https://mlsanity.com/paper/2003.06240/) (2020)
- [Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review](https://mlsanity.com/paper/1609.01915/) (2016)
- [Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus](https://mlsanity.com/paper/2012.07430/) (2020)
- [SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset](https://mlsanity.com/paper/2005.07225/) (2020)

## Built on by (most cited first, 30 of 43)

- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation](https://mlsanity.com/paper/2505.06068/) (2025)
- [Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models](https://mlsanity.com/paper/2306.02986/) (2023)
- [Mask-conditioned latent diffusion for generating gastrointestinal polyp images](https://mlsanity.com/paper/2304.05233/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [Towards General Purpose Medical AI: Continual Learning Medical Foundation Model](https://mlsanity.com/paper/2303.06580/) (2023)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs](https://mlsanity.com/paper/2504.06897/) (2025)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation](https://mlsanity.com/paper/2310.16794/) (2023)
- [Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion](https://mlsanity.com/paper/2501.16679/) (2025)
- [Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation](https://mlsanity.com/paper/2402.04031/) (2024)
- [PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps](https://mlsanity.com/paper/2205.15413/) (2022)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis](https://mlsanity.com/paper/2507.23652/) (2025)
- [Cross-modal tumor segmentation using generative blending augmentation and self training](https://mlsanity.com/paper/2304.01705/) (2023)
- [Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"](https://mlsanity.com/paper/2307.06322/) (2023)
- [Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide](https://mlsanity.com/paper/2304.05901/) (2023)
- [MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains](https://mlsanity.com/paper/2311.09401/) (2023)
- [DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading](https://mlsanity.com/paper/2404.13097/) (2024)
- [Synthetic data for unsupervised polyp segmentation](https://mlsanity.com/paper/2202.08680/) (2022)
- [Combining datasets to increase the number of samples and improve model fitting](https://mlsanity.com/paper/2210.05165/) (2022)
- [Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention](https://mlsanity.com/paper/2307.11253/) (2023)
- [Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation](https://mlsanity.com/paper/2404.06240/) (2024)
- [H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation](https://mlsanity.com/paper/2401.17104/) (2024)
- [MorphoSeg: An Uncertainty-Aware Deep Learning Method for Biomedical Segmentation of Complex Cellular Morphologies](https://mlsanity.com/paper/2409.17110/) (2024)
- [GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentation](https://mlsanity.com/paper/2501.10819/) (2025)
- [Privacy-Preserving Dataset Combination](https://mlsanity.com/paper/2502.05765/) (2025)
- [Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography](https://mlsanity.com/paper/2504.19200/) (2025)

## Abstract (arXiv)

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. Here, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional GANs because our model needs only a single image and the corresponding ground truth to train. Our method produces alternative artificial segmentation datasets with ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real and the synthetic data generated from the SinGAN-Seg pipeline, we show that models trained with synthetic data have very close performances to those trained on real data when the datasets have a considerable amount of data. In contrast, Synthetic data generated from the SinGAN-Seg pipeline can improve the performance of segmentation models when training datasets do not have a considerable amount of data. The code is available on GitHub.