# Realistic Adversarial Data Augmentation for MR Image Segmentation

arXiv: https://arxiv.org/abs/2006.13322 · HTML: https://mlsanity.com/paper/2006.13322/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 30 of 44)

- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2211.14805/) (2022)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability](https://mlsanity.com/paper/2303.01710/) (2023)
- [MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation](https://mlsanity.com/paper/2206.01737/) (2022)
- [DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2306.00499/) (2023)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)
- [DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model](https://mlsanity.com/paper/2310.12868/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2206.13737/) (2022)
- [Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation](https://mlsanity.com/paper/2108.03429/) (2021)
- [Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges](https://mlsanity.com/paper/2303.14133/) (2023)
- [Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations](https://mlsanity.com/paper/2306.10941/) (2023)
- [Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts](https://mlsanity.com/paper/2304.03209/) (2023)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation](https://mlsanity.com/paper/2203.10196/) (2022)
- [Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2107.01079/) (2021)
- [Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2409.12522/) (2024)
- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation](https://mlsanity.com/paper/2302.02504/) (2023)
- [Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation](https://mlsanity.com/paper/2304.02720/) (2023)
- [Systematic comparison of semi-supervised and self-supervised learning for medical image classification](https://mlsanity.com/paper/2307.08919/) (2023)
- [Cross-modal tumor segmentation using generative blending augmentation and self training](https://mlsanity.com/paper/2304.01705/) (2023)
- [Improved post-hoc probability calibration for out-of-domain MRI segmentation](https://mlsanity.com/paper/2208.02870/) (2022)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation](https://mlsanity.com/paper/2211.14521/) (2022)
- [Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation](https://mlsanity.com/paper/2305.19084/) (2023)
- [Exemplar Learning for Medical Image Segmentation](https://mlsanity.com/paper/2204.01713/) (2022)

## Abstract (arXiv)

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.